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we're looking for a director of business technology & ai ops to help the company turn ai into real results for our business teams. you'll identify high-impact opportunities and lead the delivery of scalable solutions with measurable business outcomes.
as we build a more autonomous, ai-first company, you'll help shape our business technology direction - from enterprise platforms to ai-native solutions-and work across business teams, it, and product & engineering to align the right capabilities and approach for each opportunity.
we believe two things matter for every leadership role at the company: leadership that sets the bar for excellence, builds high performing teams, and holds them accountable, and strategy that thinks several years ahead and turns ambitious plans into reality. these aren't buzzwords, they're how we actually work.
our ai-first approach isn't just a tagline either. we're building the future of insurance with ai at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
in this role you'll
partner with customer operations and revenue leaders to understand their goals, economics, and workflows, and shape a technology roadmap focused on measurable business impact.
lead the redesign of workflows through ai agents, automation, and software to improve performance and build more autonomous operations.
work across business teams, it, and product & engineering to align on the right approach and ownership for each initiative, balancing speed, scalability, cost, and long-term value.
lead the business technology and ai initiative portfolio: prioritize opportunities, track investments and roi, drive adoption, and ensure solutions deliver the expected outcomes.
shape the evolution of platforms such as zendesk, talkdesk, twilio, and revenue applications in partnership with business and technology leaders, identifying opportunities to simplify, consolidate, or introduce ai-native alternatives.
build and lead a strong team, defining the capabilities and operating model needed to deliver scalable solutions alongside it and product & engineering.

at the company, we're revolutionizing the way businesses interact with the digital world by revealing to them everything that happens online. our cutting-edge platform and unique data empower over 4,300 customers globally, including industry giants like google, ebay, and adidas, to make game-changing decisions that drive their digital strategies.
in 2021, we went public on the new york stock exchange, and we havent stopped growing since!
were looking for an infrastructure data engineer to join our data labs ( DL ) department, which specializes in Professional Services for our super-premium customers. this role will report to our DL (datalabs) infrastructure team manager in the r&d.
why is this role so important at the company?
we are a data -focused company, and our unique ai and Machine Learning capabilities are the center of our business.
as a data infrastructure engineer in the datalabs group, you will be the technical backbone supporting our data production, analyst, and data science teams. you will own and maintain our aws cloud accounts and databricks workspaces, optimize pipeline executions, and proactively modernize our tech stack to reduce tech debt - ensuring our platform remains scalable, reliable, fast, and cost-effective.
given the business-oriented nature of datalabs, you will work closely with cross-functional stakeholders and internal teams who interface directly with strategic customers. by unblocking operational bottlenecks and building robust tooling, you will empower our analysts and data scientists to deliver custom, high-value data products to our customers seamlessly.
so, what will you be doing all day?
environment & workspace management: administering and optimizing our groups aws cloud accounts, iam policies, and databricks workspaces to ensure peak performance, rock-solid security, and cost efficiency.
execution & pipeline infrastructure: owning the health, reliability, and scaling of our core data execution environments (airflow, docker, eks/ecs, distributed queues, and database engines).
tech debt modernization: proactively researching, benchmarking, and replacing legacy debt with modern technologies to keep our platform fast and cutting-edge.
Developer & production support: serving as the technical focal point for internal analysts and data science teams-providing the tooling, standards, and infra support they need to build custom client reports smoothly.
cross-group & client alignment: partnering with external engineering teams and internal stakeholders to support custom data delivery pipelines and uphold high-priority customer slas.
why youll love being a team member:
youll actually love the product you work with: our customers arent our only raving fans. when we asked our employees why they chose to come work at the company, 99% of them said the product.

anyone can ship an ai agent. shipping one thats right ~98% of the time, with no human checking its work, takes different engineering. thats what this team builds.
we're looking for a senior backend engineer to help push our ai customer-service agents closer to fully autonomous insurance service. you'd be joining the team behind the agent that talks to real customers every day over email, phone, chat, and sms - handling policy questions, claims conversations, and verified documents end-to-end, with no human in the loop. this isn't a demo or a copilot. real customers, real money, real stakes, every day.
we believe three things matter for every role at the company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and ai insights. these aren't buzzwords, they're how we actually work.
our ai-first approach isn't just a tagline either. we're building the future of insurance with ai at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
in this role you'll
design, build, and own production llm agent systems end-to-end - deciding how the agent thinks, what it's allowed to do, and how it fails safely
work on our Real-Time voice agent, where the hard problems are conversational: interruptions, mid-sentence tool calls, and keeping response times imperceptibly fast
shape the next generation of our knowledge base - how the agent's knowledge is structured, curated, and served, so answers stay grounded as coverage grows
ship changes as controlled experiments, measure them against thousands of replayed real conversations on our in-house eval platform, and graduate winners into production
track cost per conversation across millions of llm calls and find the wins that cut spend without cutting quality
own what the model knows and says - the customer context it sees, the limits on what it may reveal, and the guardrails against confident nonsense
build and operate scalable backend services in typescript/node on kubernetes/aws with deep observability - if you ship it, you own it
what you'll need
6+ years building production-grade backend systems that real users depend on
hands-on experience shipping llm-powered features to production, including honest reckoning with non-determinism, hallucination, and cost
an evaluation mindset - you don't call a change "better" without a measurement, and you're comfortable with the statistics that back that claim
a track record of debugging distributed systems in production, and the discipline to find root cause across service boundaries
fluency with agentic coding tools like claude code or cursor, including the habit of treating ai-generated code with a senior reviewer's skepticism
the ability to pull your own numbers, design your own experiments, and measure your own impact
ready to work in an office environment most days of the week

senior BI engineer
location: givatayim, israel experience: 5+ years work model: hybrid
we are
at the company, were revolutionizing how businesses understand the digital world by showing them everything that happens online. our unique data and solutions empower over 13,000 customers worldwide -including google, ebay, and adidas - to make game-changing decisions that drive their digital strategies. we went public on the new york stock exchange in 2021, and were still reaching new heights! come join our teammates across the globe who are bright, curious, practical, and genuinely good people.
we are looking for
we are looking for an experienced talented BI engineer to join our data team in givatayim. you will take end-to-end ownership of our data modeling, serving as the architect of the "source of truth" that powers our company's most critical strategic decisions. this is a hands-on engineering role: you will ship production code (sql, dbt, Python ) through a git-based ci/cd workflow, use ai coding tools to move faster, build and implement ai based solutionsto turn the data into a critical part of decision making.
what youll do
define the source of truth: take raw, complex data and transform it into clean, well-structured tables that product analysts and business leads can use for high-stakes decision-making. you know how to build a table that is intuitive and easy for other analysts to query.
translate strategy into logic: work with product, marketing, gtm analysts to turn abstract concepts into precise sql definitions and automated datasets.
maintain data integrity: act as the guardian of our metrics. you will ensure that the numbers used across our product and business reports are consistent, reliable, and logical.
enable high-tech initiatives: partner with our ai and data service specialists to ensure they have the structured data foundations they need to build models and external data products.
build scalable models: design, build, and maintain production-grade data models using dbt, ensuring code is modular, tested, and documented.
engineer with ci/cd discipline: treat analytics code like software. work through pull requests, automated tests and deployments (github/gitlab), code reviews, and version-controlled environments, so every change to the source of truth is tested before it ships. build with ai: use ai coding assistants (cursor, claude code) in your daily workflow to speed up modeling, testing, documentation, and code review, and help the team define how we use them responsibly. make data ai-ready: build well-documented models with clear definitions and a consistent metrics layer, so analysts, ai agents, and data products can rely on the same trusted data.
take part in building agentic solutions to support 24x7 solutions
this is a perfect job for someone who has:
experience of 5+ years of prior experience in a relevant analytics/technical role (analytics engineer, BI engineer, BI Developer or data engineer).
sql mastery: expert-level sql skills. you can write complex queries, optimize performance, and debug legacy code with ease.
dbt expertise: proven hands-on experience with dbt. you are comfortable with jinja templating, incremental models, and package management.
data modeling: experience with ETL processes and strong understanding of data warehousing concepts (amazon redshift/ snowflake) and modeling methodologies.
product & business acumen: you understand saas metrics (arr, funnels, activation, etc.) and how data logic impacts product growth and strategy.
ci/cd & git workflows: hands-on experience with git and ci processes for data projects: pull-request reviews, automated tests on every change, and automated deployments of dbt projects. ai-assisted development: practical, day-to-day experience using ai coding tools (cursor, claude code) to build faster, and the judgment to review and validate ai-generated code before it ships.

we're looking for a senior backend engineer to help build and scale the Machine Learning platform that powers how the company uses ai across the business. you'll be part of the ml platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
we believe three things matter for every role at the company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and ai insights. these aren't buzzwords, they're how we actually work. our ai-first approach isn't just a tagline either. we're building the future of insurance with ai at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
in this role you'll:
design and build the foundational ml platform and ai agents to accelerate data science model delivery across all business units
architect cloud-native microservices running on kubernetes, using infrastructure-as-code to automate model deployment and management
own the end-to-end ml lifecycle, covering training, testing, deployment, and Real-Time monitoring
evaluate and choose the right tools and technologies based on workload demands and performance requirements
collaborate with engineering, data science, and product teams to keep ml projects aligned with business goals
identify and fix reliability, scalability, and performance gaps before they become problems.

we're looking for a senior backend engineer to help build and scale the Machine Learning platform that powers how the company uses ai across the business. you'll be part of the ml platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
we believe three things matter for every role at the company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and ai insights. these aren't buzzwords, they're how we actually work. our ai-first approach isn't just a tagline either. we're building the future of insurance with ai at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
in this role you'll
design and build the foundational ml platform and ai agents to accelerate data science model delivery across all business units
architect cloud-native microservices running on kubernetes, using infrastructure-as-code to automate model deployment and management
own the end-to-end ml lifecycle, covering training, testing, deployment, and Real-Time monitoring
evaluate and choose the right tools and technologies based on workload demands and performance requirements
collaborate with engineering, data science, and product teams to keep ml projects aligned with business goals
identify and fix reliability, scalability, and performance gaps before they become problems.

we're looking for a data warehouse tech lead to drive the technical vision and execution of our data infrastructure that powers decision-making across all of the company.
you'll lead both the technology and the business coordination for our data warehouse - architecting scalable solutions while working closely with stakeholders and data providers to ensure our platform serves the entire organization's needs. this role combines deep technical leadership with strategic business partnership as we build the company's next-generation data stack.
we believe three things matter for every role at the company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and ai insights. these aren't buzzwords, they're how we actually work.
our ai-first approach isn't just a tagline either. we're building the future of insurance with ai at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
in this role you'll:
lead technical architecture - design and develop scalable data warehouse solutions that support multiple products and serve the entire organization's analytics needs
manage the technical roadmap - set strategy and guide execution for the data warehouse team, ensuring our platform evolves with business requirements
drive business process coordination - translate business needs into technical requirements while establishing clear data contracts with r&d, analytics, and external data providers
establish and implement best practices - set technical standards for data warehouse architecture, performance tuning, and development methodologies that guide the entire team's approach to building scalable data solutions
create and maintain sustainable data pipelines - build resilient systems capable of handling unstructured data and managing an evolving schema registry across diverse data sources
implement advanced data modeling - create robust data structures using methodologies like dimensional modeling, and optimize ETL /elt processes for our semantic layer
establish data quality standards - build processes for schema evaluation, anomaly detection, and monitoring data completeness and freshness across all sources
lead cross-team collaboration - work directly with data engineers, ml platform engineers, data scientists, analysts, and product managers to align technical solutions with business goals

we're looking for a backend engineer to help build and scale the Machine Learning platform that powers how the company uses ai across the business. you'll be part of the ml platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
we believe three things matter for every role at the company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and ai insights. these aren't buzzwords, they're how we actually work. our ai-first approach isn't just a tagline either. we're building the future of insurance with ai at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
in this role you'll:
design and build the foundational ml platform and ai agents to accelerate data science model delivery across all business units
architect cloud-native microservices running on kubernetes, using infrastructure-as-code to automate model deployment and management
own the end-to-end ml lifecycle, covering training, testing, deployment, and Real-Time monitoring
evaluate and choose the right tools and technologies based on workload demands and performance requirements
collaborate with engineering, data science, and product teams to keep ml projects aligned with business goals
identify and fix reliability, scalability, and performance gaps before they become problems.

were looking for a senior-level backend engineer to join our growing engineering team in tlv.
this role will require you to bring features to the finish line while collaborating closely with other developers, as well as with the product, QA, data, and Machine Learning teams-all with the goal of delivering a seamless, joyful experience for our customers.
we believe three things matter for every role at the company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and ai insights. these aren't buzzwords, they're how we actually work.
our ai-first approach isn't just a tagline either. we're building the future of insurance with ai at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
in this role youll:
help build our products using microservices written in typescript (using nestjs).
leverage our in-house autonomous tools to manage all development flow, from setting up our development environment to creating git branches and deploying services to production
collaborate closely with the product team, in a self-sufficient squad to achieve the squads okrs
be the owner of your domain to ensure code quality, performance, and scalability of our products
coach and mentor other developers and help them elevate their skills.

the company harnesses the power of ai to transform how revenue teams win. the company revenue ai operating system unifies data, insights, and workflows into a single, trusted system that observes, guides, and acts alongside the worlds most successful revenue teams. powered by the company revenue graph, ai-powered intelligence, specialized agents, and trusted applications, the company helps more than 5,000 companies around the world deeply understand their teams and customers, automate critical sales workflows, and close more deals with less effort. for more information, visit company.
at the company, you will join a company built on innovative products, ambitious goals, and passionate people. we are shaping the future of revenue intelligence and we want people who are excited to build what comes next. you will work with a team that dreams big, moves fast, and cares deeply about the craft and about each other. here, transparency and trust are core to how we operate, and every person has the opportunity to make a visible impact. if you want to grow, stretch, and do work that truly matters, the company is the place to do the best work of your career.
software architect, ai applications
as an architect in the company's ai applications group, you will lead the architecture and technical direction of the revenue harness, the agentic framework that powers an ai teammate for every revenue professional. the assistant understands a user's deals, customers, and pipeline. it can reason, plan, and act on the user's behalf, grounded in the richest revenue data in the world.
this is a hands-on role with broad influence. you will embed with teams where needed, guide design decisions, and help build scalable, extensible agentic systems on state-of-the-art ai technology.
what you'll build
agentic core: agent orchestration, planning and reasoning loops, multi-step execution, and multi-agent coordination for long-running revenue workflows.
tools & integrations: a secure, extensible tool ecosystem, including mcp-based tools and connectors, that lets agents read from and act on the company data, crms, email, and calendars.
context & memory: retrieval pipelines over calls, emails, deals, and accounts, using rag, hybrid search, and knowledge graphs, plus short- and long-term memory.
quality & trust: offline and online evaluation of accuracy, groundedness, and task success, along with guardrails, permissions, and human-in-the-loop controls.
model platform: multi-provider model routing, streaming, llm observability, and cost and latency management.
runtime & execution: high-concurrency infrastructure for stateful and stateless agents, including lifecycle management, isolated workers, task queues, and distributed state persistence.
deployment & ci/cd: cloud-native kubernetes deployments with multi-region rollout, zero-downtime updates, canary releases for non-deterministic workflows, and strict tenant sandboxing.
you'll own
revenue harness architecture: serve as the group's central technical leader. define the reference architecture and the contracts between agents, tools, context, and models, so that teams build on a shared, scalable foundation.
hands-on engineering: embed in teams across domains and contribute directly to design and code.
quality & reliability standards: set the group's eval methodology, regression gates, and production monitoring for non-deterministic systems.
mentorship & engineering standards: raise the bar on design quality and best practices across the group.
ai-assisted development: drive adoption of coding agents, ai code review, and agentic workflows to improve velocity and quality.
you'll solve
reliability on non-deterministic foundations: make llm behavior trustworthy through structured outputs, tool-call validation, fallbacks, and well-defined failure modes.

at the company, we are revolutionizing the way businesses interact with the digital world by revealing to them everything that happens online.
our unique data and solutions empower over 4,300 customers globally, including industry giants like google, meta, openai, to make game-changing decisions that drive their digital strategies. in 2021, we went public on the new york stock exchange, and we continue to reach new heights! come work alongside our teammates across the globe who are bright, curious, practical, and good people.
were looking for a sharp and driven data analyst to join our fast - paced data labs team. as part of a high-performing group of analysts, youll handle complex, custom requests from some of the biggest names in the market, turning raw data into strategic, ad hoc insights. the work is intense and constantly evolving - it requires strong analytical skills and the ability to work independently under pressure. youll need to be curious, adaptable, and willing to dive deep to find answers where others might stop. if you thrive in a demanding environment and are excited by the opportunity to make a real impact, we want you on our team.
what does the day to day of a Big Data analyst customer facing ( data labs) at the company look like:
building custom-made reports end to end - from meeting with the client and understanding their needs, to writing the code and setting up the report for ongoing delivery.
apply your expertise in quantitative analysis and data mining to turn data into insights.
conduct research and develop tools which will help answer client's business. questions by using the company's raw data sets and algorithms.
partner with engineering teams to establish an infrastructure to scale solutions.
partner with advisory services team of consultants to answer strategic business questions and deliver custom data products seamlessly
this is the perfect job for someone who has:
analyzing billions of user-based events into meaningful insights for strategic clients.
why youll love being a team member:
youll actually love the product you work with: our customers arent our only raving fans. when we asked our employees why they chose to come work at the company, 99% of them said the product. imagine how exciting your job is when you get to work with the most powerful digital intelligence platform in the world.
youll find a home for your big ideas: we encourage an open dialogue and empower employees to bring their ideas to the table. youll find the resources you need to take initiative and create meaningful change within the organization.

the company harnesses the power of ai to transform how revenue teams win. the company revenue ai operating system unifies data, insights, and workflows into a single, trusted system that observes, guides, and acts alongside the worlds most successful revenue teams. powered by the company revenue graph, ai-powered intelligence, specialized agents, and trusted applications, the company helps more than 5,000 companies around the world deeply understand their teams and customers, automate critical sales workflows, and close more deals with less effort. for more information, visit company.
at the company, you will join a company built on innovative products, ambitious goals, and passionate people. we are shaping the future of revenue intelligence and we want people who are excited to build what comes next. you will work with a team that dreams big, moves fast, and cares deeply about the craft and about each other. here, transparency and trust are core to how we operate, and every person has the opportunity to make a visible impact. if you want to grow, stretch, and do work that truly matters, the company is the place to do the best work of your career.
as a senior backend application infrastructure engineer, youll build and evolve the core libraries, frameworks, and services that power the company's Python platform at scale. youll work on backend systems, distributed architectures, data infrastructure, and event-driven systems, enabling engineering teams to build reliable and scalable products efficiently.
were looking for a strong Python engineer with deep systems understanding who enjoys solving complex technical challenges and building the foundations behind large-scale production environments.
youll own:
designing, building, and maintaining core Python infrastructure libraries and frameworks used across engineering teams.
developing and supporting high-scale microservices and the shared foundations they run on.
building reusable infrastructure capabilities for data access, messaging, observability, and service communication.
driving engineering standards around performance, observability, security, maintainability, and production readiness.
driving ci/cd processes, deployment automation, and cloud-native operational practices.
youll solve:
performance, concurrency, and scalability challenges across mission-critical Python services.
data and infrastructure challenges across postgresql, mongodb, opensearch, redis, kafka, and sqs.
production issues that require deep understanding of runtime behavior, such as memory, cpu, i/o, and process and thread behavior.
the growing complexity of modern software ecosystems by building infrastructure that guides developers toward best practices while preserving flexibility where needed.
bridging architectural and operational gaps between backend, DevOps, sre, and product engineering teams.
youll impact:
faster product development through strong infrastructure libraries and foundations.
stable, scalable, and efficient production systems across critical platform components.
better Developer experience through well-designed, reusable components and streamlined engineering workflows.
the long-term evolution of the company's Python infrastructure and engineering standards.

the company harnesses the power of ai to transform how revenue teams win. the company revenue ai operating system unifies data, insights, and workflows into a single, trusted system that observes, guides, and acts alongside the worlds most successful revenue teams. powered by the company revenue graph, ai-powered intelligence, specialized agents, and trusted applications, the company helps more than 5,000 companies around the world deeply understand their teams and customers, automate critical sales workflows, and close more deals with less effort. for more information, visit company.
at the company, you will join a company built on innovative products, ambitious goals, and passionate people. we are shaping the future of revenue intelligence and we want people who are excited to build what comes next. you will work with a team that dreams big, moves fast, and cares deeply about the craft and about each other. here, transparency and trust are core to how we operate, and every person has the opportunity to make a visible impact. if you want to grow, stretch, and do work that truly matters, the company is the place to do the best work of your career.
as a senior backend engineer on the company's ai framework team, you will build the foundational platform that enables the entire organization to design, evaluate, deploy, and operate ai agents at scale. this is a new, high-impact team responsible for creating the frameworks, tools, and guardrails that power the company's agentic ecosystem.
youll work deeply in the agentic space, designing systems for orchestration, evaluation, observability, and safety. the platforms you build will be used by multiple teams across the company, making your impact immediate and company-wide.
youll own:
agentic framework architecture: designing and building the company's internal agentic framework, leveraging and integrating industry-standard tools such as langchain, langsmith, adk, and similar ecosystems.
evaluation and quality systems: building evaluation frameworks and workflows for ai agents, including offline and online evaluations, quality metrics, regression detection, and experimentation infrastructure.
observability, monitoring, and guardrails: providing the organization with robust observability capabilities for ai agents, including tracing, logging, monitoring, cost tracking, and safety guardrails to ensure reliable and responsible usage.
Developer enablement platforms: creating apis, sdks, and abstractions that enable product teams to easily build, TEST, and operate agents while adhering to platform standards.
cross-language integrations: designing integrations and tooling across Python and JAVA to enable seamless adoption of the ai framework within the company's broader backend ecosystem.
agent runtime and execution engine: building the runtime responsible for orchestrating agent execution, managing tool calls, maintaining state and memory, and ensuring reliable execution across distributed systems.
youll solve:
agent lifecycle and orchestration complexity: managing agent execution, tool usage, memory, workflows, and failure modes in production-grade systems.
ai system reliability at scale: ensuring agents remain observable, debuggable, and safe as usage scales across teams and products.
evaluation and drift challenges: detecting quality regressions, model behavior changes, and unintended agent behaviors through robust evaluation and monitoring systems.
platform adoption friction: balancing flexibility with guardrails so teams can innovate quickly without compromising reliability, security, or cost controls.
youll impact:
company-wide ai enablement: empowering every engineering team at the company to build agent-based solutions faster, with higher quality and confidence.
foundational ai infrastructure: establishing the core frameworks, evaluations, and observability standards that all ai agents at the company will rely on.