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As a Principal Data Engineer, you will be integral to the security research group, designing, building, and maintaining the data pipelines and data frameworks that are the backbone of our cyber attack detection capabilities. You will work closely with top security and AI researchers, enabling their work and seeing the direct impact of your contributions on our services and the day-to-day effectiveness of the research team.
Key Responsibilities
Lead the design and implementation of end-to-end, complex data pipelines and data platforms in cloud environment.
Collect, analyze, and translate complex data requirements from various stakeholders including data analysts, developers, and Cyber security researchers.
Develop robust, scalable Cloud based data solutions using Python and distributed processing frameworks such as Apache Beam (Dataflow) , GKE , Spark , etc.
Utilize AI as part of the development cycle, including generating, reviewing, and testing code via prompts.
Model and manage data within cloud MPP databases and data lakes such as BigQuery, Redshift, or Snowflake.

As a Data Scientist, you will apply your expertise in software engineering, LLM agents, and cybersecurity to build new defense techniques against cyber-villains. You will be a driving force in designing and developing groundbreaking, Agentic LLM-based security solutions that block attacks before they begin. Join our innovative team to shape the future of cybersecurity and impact millions of customers.
Important: this role requires a unique combination of hands-on software engineering skills as well as previous experience with designing and delivering LLM-driven products.
Key Responsibilities
Contribute to a diverse and highly skilled research group that pioneers state-of-the-art technologies to protect our customers.
Utilize analytical rigor, statistical methods, programming, and data modeling to analyze vast amounts of data, applying your cybersecurity knowledge to guide our focus and approach.
Design and implement agentic LLM solutions to face modern cybersecurity challenges.
Decompose complex problems scientifically and provide actionable insights and recommendations to both technical and non-technical stakeholders.
Lead and collaborate on end-to-end projects within the team and across engineering and product teams, from initial ideation to final deployment.

Were looking for a BI & Data Engineering Team Lead to join our BI team and take end-to-end ownership of high-impact analytics foundations while mentoring a team of 4-6 talented BI Data Engineers. This role sits at the core of how measures success, makes decisions, and scales-turning raw data into trusted, business-critical insights used across Product, GTM, and Finance.

In this role, you'll combine people leadership with hands-on technical depth (roughly 30% of your time), owning the roadmap, architecture, and delivery of BI platform. Youll shape how AI and agentic workflows transform the way BI is built and consumed, helping raise the bar for analytics engineering across the company.

Hands-on experience using GenAI to improve analytics engineering workflows, automate development processes, and increase delivery speed is a must for this role.

This role is based in Tel Aviv. We work in a hybrid model, with 3 days a week in the office.

This might be for you if:

You enjoy owning data foundations end to end - from raw data to semantic layers while leading and growing people
You like turning ambiguous business questions into clear, governed metrics and scalable data models
You care about data quality, performance, and trust at scale, as well as optimization and FinOps practices
You enjoy mentoring, setting technical standards, and leading by example with a hands-on approach (~30%)
You actively leverage AI tools and agentic automations to improve development speed, analytical accuracy, and metric governance

The Data Group builds and maintains the core data assets that power our products, from our Company and Contact databases to our Connect and Recommendation systems. The Data Tech Lead sits at the forefront of that mission, leading engineering innovation that directly shapes the experience of our growing user base and drives the evolution of intelligent data services.

This role is based in Tel Aviv. We work in a hybrid model, with 3 days a week in the office.

Your impact and responsibilities:

Leading the design and development of scalable, high-performance data workflows, including both batch pipelines and real-time data products.
Defining, implementing, and enforcing engineering best practices related to code quality, testing, CI/CD pipelines, observability, and documentation.
Mentoring, supporting, and growing a team of data engineers, fostering a collaborative and high-performance engineering culture.
Identifying opportunities to create new data assets and features that expand product capabilities and value proposition.
Driving architectural decision-making in areas of data modeling, storage solutions, and compute resources within cloud environments such as Databricks and Snowflake.
Collaborating closely with cross-functional stakeholders- including Product, DevOps, and R&D - to ensure effective delivery and platform stability.
Promoting and championing a data-driven mindset across the organization, balancing technical rigor with business context and strategic goals.

What you will do
You will lead a team of experienced Data Scientists while remaining deeply involved in the technical work.

This is a hands-on leadership role (~70% hands-on) combining direct modeling work with ownership of team direction and execution.

You will work on core systems that operate at a massive scale, where:

Data is abundant, but labels are scarce and expensive

problems are long-tail and ambiguous

Systems must meet strict latency and cost constraints (pre-bid)

Your responsibilities include:

Lead development of content classification systems across social platforms (Meta, TikTok, YouTube), web, and apps

Design and build models across computer vision, NLP, and multimodal pipelines

Own the full lifecycle: data selection -> labeling strategy -> training -> evaluation -> deployment

Develop strategies for efficient data curation and labeling (active learning, auto-labeling, sampling under scale)

Improve model quality (precision/recall) while balancing cost, latency, and scale

Drive automation systems (auto-labeling, auto-curation, retraining loops)

Apply modern AI approaches (LLMs, embeddings, foundation models) to real production problems

Lead and mentor a team of senior Data Scientists, setting technical direction and pushing execution forward

Work closely with ML Engineering, Product, and Policy to translate ambiguous requirements into scalable systems

We are looking for an Experienced Backend Engineer to join our Data Foundations team.

What Youll Do:
Dive into the heart of engineering core as a pivotal member of our team, dedicated to constructing the internal tools and services that are the cornerstone of our data-centric universe. Your role is at the intersection of innovation and collaboration, focusing on creating sophisticated solutions that streamline data versioning, enhance data processes, and facilitate seamless interactions across all data teams. In this team, you will be building the robust framework that enables all other teams to manage and leverage data with unprecedented efficiency and accuracy. Your contributions will not only improve our internal workflows but also amplify the impact of our data on client success.

We are looking for a Data Engineering Team Lead to drive the development and optimization of our data infrastructure.


So, what will you be doing all day?
Team Management

Serve as the direct manager for data engineers, providing regular feedback, career guidance, and technical mentorship.
Translate business requirements into actionable technical roadmaps; prioritize and assign daily tasks using sprint methodologies.
Cultivate a collaborative, high-performance team culture focused on continuous improvement and engineering excellence.
Cultivate a collaborative, high-performance team culture rooted in a delivery mindset, a business-driven focus, and engineering excellence.


Hands on Engineering

Design, build, and maintain robust, scalable data pipelines capable of processing massive datasets.
Oversee and maintain multiple development and production environments to ensure seamless deployment and high availability.
Design and implement highly cost-efficient cloud data infrastructure, constantly monitoring and optimizing resource utilization.
Research, develop, and integrate AI models and autonomous agents into the data ecosystem to automate pipeline monitoring, optimize data curation, and unlock intelligent data-driven capabilities.

Were looking for a Big Data Engineer to develop and integrate systems that retrieve, process and analyze data from around the digital world, generating customer-facing data. This role will report to our Team Manager, R&D.

So, what will you be doing all day?

Your role as part of the R&D team means your daily responsibilities may include:

Design and implement complex high scale systems using a large variety of technologies.
You will work in a data research team alongside other data engineers, data scientists and data analysts. Together you will tackle complex data challenges and bring new solutions and algorithms to production.
Contribute and improve the existing infrastructure of code and data pipelines, constantly exploring new technologies and eliminating bottlenecks.
You will experiment with various technologies in the domain of Machine Learning and big data processing.
You will work on a monitoring infrastructure for our data pipelines to ensure smooth and reliable data ingestion and calculation.

Were looking for a Senior Data Engineer in the Data Collection Ingest team to contribute to the design and coding of data ingestion services & pipelines. This role involves working as part of a team that handles millions of requests per minute across multiple servers, and is responsible for a wide-range of data pipelines, processing billions of events each day.

So, what will you be doing all day?

Your role as Data Engineer of the Ingest Data Collection team means your daily responsibilities may include:

Design, code and manage end-to-end data ingestion pipelines, both online and offline.
Take charge of developing & maintaining modern data infrastructure, while implementing best practices for building data pipelines.
Be responsible for high-scale ingestion services, solving challenges of availability, reliability, and scalability.
Run the production environment by monitoring availability and taking a holistic view of system health and data quality.
Own data infrastructure features from design to production using industry best practices with focus on quality and delivery.
Lead design & decision-making processes of the team.
Solve diverse complex problems of scale, performance and business logic.
Collaborate with product managers and other team leaders to plan, nurture, and implement an efficient and effective development process.
Continuously learn and evaluate new technologies in the everlasting effort to perfect our products
Perform code reviews, evaluate implementations, and provide feedback about potential improvements.
Improve your skills, learn from and mentor top-notch engineers and enrich other team members.
Have lots of fun!

We're looking for a Senior Data Product Manager to drive the strategy, architecture, and execution of the data that power the Retail Intelligence products suite, eCommerce analytics product.

This role sits at the intersection of data infrastructure and customer value - you'll own the datasets, pipelines, and methodologies that help leading brands and retailers make confident decisions about their commerce assets.


What youll experience on this team?

Work with top talent across data, engineering and product teams.
Ship AI-powered capabilities iteratively, improving with every release.
Work with latest technology in an ever-evolving work environment
Operate with a startup mindset: move fast, think big, and build long-term value for customers.
Have direct impact on how customers make business decisions
What youll do as a Senior Data Product Manager?

Own the vision, roadmap, and delivery of the datasets that underpin Retail Intelligence, from eCommerce traffic and conversion data to product-level insights and category benchmarks.
Lead AI integration into internal processes and core features that speed customer decisions.
Define and track data quality metrics (completeness, coverage, freshness, accuracy) and establish validation, logging, and governance frameworks.
Lead modularization of data structures to enable scalability, and continuous improvement.
Stay close to customers and the market through ongoing research and interviews.
Partner with engineering, data science, design, sales, and marketing to deliver and drive adoption.

Required Senior BI Analyst
About the role:
Data only creates value when people can see it, trust it, and act on it. As Senior BI Analyst, you own that last mile: the BI platform everyone opens to answer their questions, and the KPI definitions that make sure the numbers on every dashboard mean the same thing across every team.
This is the business-facing anchor of our analytics function. You'll build and govern the reporting layer, define the metrics that leadership runs the company on, and lead the Business Analyst guild - the network of domain analysts embedded across GTM, Finance, CS, and Product. You're equal parts platform owner, metrics authority, and enabler of the analysts around you.
If you love turning messy business questions into clear, trusted, self-serve answers - and raising the analytical bar for a whole company while you do it - this role is for you.
What youll own:
Own the BI platform end-to-end - Design and manage the dashboard architecture, set standards for report building and certification, manage access/governance, and keep the platform performant and trusted
Define and govern company-wide KPI metrics - Build a certified KPI catalog with documented logic and ownership; arbitrate metric definitions across teams so "win rate" means the same thing everywhere and is encoded once in the semantic layer
Drive self-serve analytics adoption - Empower stakeholders to answer their own questions by making the platform accessible, intuitive, and built on trusted definitions
Co-lead the Analytics guild - Convene and lead the company-wide community of practice connecting domain analysts; run enablement, tooling decisions, and reviews to keep everyone aligned on standards and the certified KPI catalog
Bridge analytics and engineering - Partner with the AI Data Engineer to ensure business definitions translate consistently into technical implementations across dashboards and AI agents
What you'll do day to day:
Design, build, and maintain governed dashboards and reports that stakeholders actually use.
Curate the certified KPI catalog - definitions, logic, ownership, and change control.
Partner with GTM, Finance, CS, and Product to translate business questions into clear, reusable metrics.
Drive self-serve BI adoption and reduce ad-hoc reporting requests.
Run the Business Analyst guild: standards, reviews, enablement, and tooling.
Work with data engineering to keep KPI definitions aligned with the semantic layer and underlying models.
Own the health of the BI platform: access, performance, certification, and cleanup.

Required AI Data Engineer
About the role:
We're building the intelligence layer that lets everyone ask hard questions of our data and get trustworthy answers - in plain language, in seconds. As AI Data Engineer, you own the semantic and AI-native surface of our Snowflake platform: the governed semantic layer that defines "what a metric means," the Cortex Agents that let business users query it conversationally, and the infrastructure that keeps all of it fast, reliable, and ready to scale.
This is a builder-owner role. You'll ship the semantic models, wire up the agents, and set the standard for how analysts across the company work with data. You'll also lead the Data Analyst guild - the connective tissue that keeps our hub-and-spoke analytics model coherent as it grows.
If you're excited by the space where data engineering, LLM tooling, and analytics governance meet, this role sits right in the middle of it.
What youll Own:
Snowflake semantic layer - Own the semantic layer end-to-end as the single source of truth for metrics; design semantic models, enforce naming standards, and ensure consistent metric definitions across dashboards and AI agents.
Cortex Agents - Design and deploy conversational AI agents using Cortex Analyst and Cortex Search; tune for accuracy and safety, expose through multiple surfaces (Snowflake Intelligence, Streamlit, MCP), and build evaluation harnesses to maintain quality at scale.
Data craft (Analytics guild) - Co-lead the technical track of the Analytics guild; set SQL and modeling standards, run technical enablement and code reviews, and serve as the technical authority for analysts.
Scaling data infrastructure - Improve performance, reliability, cost efficiency, and governance across the dbt/Airflow/Airbyte/Snowflake stack as data volume and query load grow; optimize warehouse sizing, medallion layers, and ingestion pipelines.
What you'll do day to day:
Model and maintain semantic views that power both dashboards and AI agents, keeping definitions versioned, tested, and certified.
Build, evaluate, and iterate on Cortex Agents - including retrieval quality, guardrails, and observability.
Extend and optimize dbt models, Airflow DAGs, and Airbyte connectors across bronze/silver/gold layers.
Partner with GTM, Finance, CS, and Product stakeholders to translate business questions into governed, reusable data assets.
Run the Data Analyst guild: standards, reviews, enablement, and tooling.
Own data quality, lineage, and cost monitoring across the Snowflake platform.
Expose data and agents through Streamlit apps, BI tools (Omni), and MCP servers for internal AI workflows.

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 our Data Operations Team Leader!
This person will oversee a team of data operations specialists responsible for labeling data for machine learning and AI projects.
This role involves managing day-to-day operations, ensuring annotation quality and accuracy, developing guidelines, and collaborating with cross-functional teams to meet project deadlines and objectives.
What Youll Do:
Manage the end-to-end data workflow, from incoming data through annotation delivery to the ML teams, including overseeing data upload processes and ensuring data availability.
Manage database operations and ensure the integrity and availability of annotation data.
Become a Subject Matter Expert in our data and actively participate in hands-on annotation projects.
Create and maintain onboarding materials, annotation guidelines, and operational documentation to support knowledge sharing and consistent annotation practices.
Lead a team of Data Operations Specialists by managing task assignments, scheduling, and day-to-day execution. Provide the tools and processes needed for efficient work, ensure high annotation quality in collaboration with the ML teams, and continuously improve workflow efficiency
Own the annotation platform, driving bug fixes, feature development, and workflow improvements using Python and GenAI tools.
Develop and maintain operational dashboards and real-time performance metrics to monitor team efficiency, quality, and output.
Collaborate closely with data scientists, machine learning engineers, and product managers to understand annotation requirements and project goals. Coordinate cross-functional communication, monitor operational KPIs, and oversee daily operations to keep projects on track and ensure adherence to shifting schedules.

We are now hiring a talented, self-driven and passionate Senior Data Engineer to build and maintain optimized and highly available data pipelines that facilitate deeper analysis and reporting.
What Youll Do:
Design, develop, and maintain scalable data pipelines that integrate data from multiple sources, including APIs, databases, streaming platforms, edge computing devices, cloud services, and on-premise systems.
Design reliable and business-critical data processing workflows for batch and real-time use cases.
Develop data access services and APIs that enable efficient communication between edge devices, cloud infrastructure, and on-premise environments.
Design and optimize storage solutions for structured, semi-structured, and high-dimensional sensor data to support AI model training, inference, and analytics.
Strong understanding of distributed systems and scalable data processing architectures.
Build and maintain scalable streaming data pipelines for low-latency processing and event-driven architectures.
Analyze existing data architecture, storage models, and processing workflows, and continuously improve performance, scalability, reliability, and maintainability.
Optimize cloud infrastructure and data storage costs while maintaining high availability and low-latency access.
Collaborate closely with Software, AI, Algorithms, DevOps, and Product teams to translate business requirements into scalable technical solutions.
Design monitoring, observability, and operational processes for data platforms.