דרושים » דאטה » data engineer - applied ml

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.

We are looking for a hands-on Data Lead to build and own our data domain end-to-end.
This is a key role for someone who combines strong engineering capabilities, deep data passion, product thinking, and business understanding. We are looking for a builder who can independently create the data infrastructure, deliver reliable dashboards and insights, support product and business decision-making, and turn data into a strategic asset for the company.
This is not just a reporting or BI role. You will be responsible for building the data foundation that will support our next stage of growth- across infrastructure, product analytics, business insights, executive reporting, customer-facing analytics, and future AI-driven use cases.
What youll do:
Own our data domain end-to-end- from infrastructure and pipelines to dashboards, insights, and business impact.
Build and maintain scalable data pipelines across product, finance, GTM, customer, and operational data sources.
Design reliable data models and create a clear source of truth for key company metrics.
Build business and executive dashboards that help leadership understand the state of the business.
Analyze product usage, engagement, adoption, retention, feature performance, and customer value.
Support Product, R&D, Sales, Marketing, Customer Success, Finance, and leadership with meaningful data insights.
Translate business and product questions into clear data analysis and actionable recommendations.
Work closely with R&D, while being technically independent and able to build and deliver solutions without relying on engineering support for every step.
Define data standards, documentation, quality checks, metric definitions, and best practices.
Use modern AI tools to accelerate data development, analysis, documentation, automation, and insight generation.
Identify opportunities where our data can support smarter product experiences, automated insights, customer-facing analytics, and AI-supported decision-making.

Required Senior Data Engineer
21679 ID
Role Description:
As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.
Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.
Key Job Responsibilities and Duties:
Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.
Dealing with massive textual sources to train GenAI foundation models.
Solving issues with data and data pipelines, prioritizing based on customer impact.
End-to-end ownership of data quality in our core datasets and data pipelines.
Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.
Providing tools that improve Data Quality company-wide, specifically for ML scientists.
Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.
Acting as an intermediary for problems, with both technical and non-technical audiences.
Promote and drive impactful and innovative engineering solutions
Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

As a AI-Data Engineer, youll take a leading role in building the foundation of our companys Agentic Exposure Management platform: the source of truth that tells a security team what they have, whats actually risky and why, how to fix it, and who is responsible.
Enterprises run their security across a sprawl of cloud and on-prem tools. Making sense of that sprawl is the heart of what we do. Were building a complex data fabric that reconciles data from a hundred different sources, applies sophisticated transformation and enrichment, and turns partial, uncertain signal into one trustworthy model of an organizations real exposure.
Key Responsibilities:
Model the security graph. Design an ontology that spans both the modern cloud and the classical on-prem world, abstract enough to absorb 100+ different integrations under one model, yet rich enough to carry everything needed for decision making. Decide whats an entity, whats an edge, and whats a denormalized attribute, across millions of assets and finding rows.
Own asset identity engineering and reconciliation. Upstream identifiers are uncertain and volatile. Engineer deterministic identity resolution for assets and findings arriving from different vendors.
Build LLM-based and agentic pipelines for data classification, entity extraction, and enrichment, optimizing for cost, latency, and precision in production.
Build the integration factory. Stand up a scalable integration platform that drives down time-to-integration.
Build and maintain complex ETL pipelines handling millions of records: incremental sync, complex correlation, and data quality observability and monitoring.

Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.

Key responsibilities

Lead a team of data scientists, data engineers and product analysts

Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation

Drive excellence, code quality, and best development practices

Provide technical direction and hands-on guidance for complex AI systems

Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences

Ensure AI solutions are safe, scalable, observable, and continuously improving

Lead initiatives around product data analysis and experimentation

Analyze user interactions with product features to improve accuracy, UX, and business impact

Partner with product teams to define metrics, dashboards, and experiments that guide product improvements

Design system architectures for AI-enabled applications at scale

Evaluate and select technologies for AI platforms and data pipelines

Guide the development of prompt engineering frameworks and centralized prompt management

Ensure robust monitoring, evaluation, and feedback loops for AI outputs

Translate product and business goals into technical roadmaps and execution plans

Drive alignment between AI capabilities and measurable product outcomes

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacks-its about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes-designed for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.

we are on a mission to create public transportation systems that provide far greater access to jobs, healthcare, and education. Our platform serves as the technology backbone for modern transit networks, transforming antiquated and siloed public transportation systems into smart, data-driven, and efficient digital networks. With hundreds of agency partners around the world, we are recognized as the leading transportation technology and service provider globally.
As a Software & Data Engineer at our company, you will play a central role in building and evolving the data infrastructure that powers one of the world's leading transportation platforms. You'll work with rich, complex transportation data - from ride bookings and payments to real-time operational signals - designing the pipelines and systems that turn raw data into clean, reliable, and actionable insight across the business. This is a high-ownership role on a talented and deeply motivated engineering team in Tel Aviv, where your work will directly shape how our company understands and improves its service for cities and riders around the world.
About the Role:
Design and build highly scalable, reliable data pipelines that serve clean, structured data across our company's engineering, product, and business teams - ensuring the entire organisation can trust the data it works with
Own the architecture of complex data models that translate messy, real-world transportation data - bookings, payments, driver activity, and more - into systems that are fast, efficient, and built to scale
Lead end-to-end development across the full data lifecycle: from architecture and design through to deployment, monitoring, and continuous improvement
Proactively monitor data quality and reliability, identifying and resolving discrepancies before they reach stakeholders - building the kind of data infrastructure people can depend on
Collaborate with a broad forum of engineers, analysts, and business stakeholders to identify data needs, design POCs, and ship scalable solutions that make a real difference to how our company operates
Contribute to the adoption and evolution of our modern data stack, including DBT, Airflow, Iceberg storage, and AWS big data tooling such as Glue, EMR, and Athena.

We are looking for an experienced and hands-on Backend Engineer to be a key player in building high-scale Data Platforms and Products for our business teams.
This role involves working with large datasets and scalable systems, and developing internal tools to enable data-driven decision-making across the company.
The team builds the backend systems and data infrastructure the rest of the company relies on: internal products, APIs, event pipelines, and integrations with external tools. The data comes from the full product, at the volume of over 1.7 million daily active users. The features are multi-system: an API, a pipeline, and often LLM-backed flows that have to stay correct in production.
LLMs and agents are part of the job. We use them to write, review, and debug code, and we ship them inside the product: agents and LLM features that other teams depend on.
Key Responsibilities:
Develop internal tools and products for teams across Connecteam.
Build and maintain services and APIs that support complex, multi-step workflows.
Operate in a real-time, event-driven environment at high volume.
Create and manage data pipelines, including the checks that keep data trustworthy.
Design and ship LLM and agent features: tool use, prompts, evaluation, and the limits around cost, latency, and quality.
Use coding agents as part of normal delivery, and raise the quality of what they produce.
Take ownership of multiple systems and products.
Work in a multi-cloud environment (Azure/GCP/AWS).
Integrate third-party tools with our platform.
Translate ambiguous business requirements into a technical plan and lead it through to production.

We are seeking a Senior Backend Engineer - Data Platform to join our expanding team and play a crucial role in designing, building, and maintaining robust and scalable data pipelines and infrastructure. In this role, you will directly enable data-driven decision-making and support the development and deployment of AI/ML products that power Health.
Youll collaborate closely with engineering, product, and data science teams to ensure our data systems are high-quality, resilient, and scalable as we grow. As a Senior Backend Engineer on our Data Platform team, you will drive efforts to deliver reliable, efficient, and consistent data services across the organization. You will also help enable the rapid development and deployment of advanced features, insights, and AI-driven capabilities that improve outcomes for clinicians and clients.