דרושים » דאטה » Data Engineer

Required ML Data Engineer
Israel: Tel Aviv/ Hybrid (Israel)
R&D | Full Time | Job Id: 24792
Key Responsibilities
Your Impact & Responsibilities:
As a Data Engineer - AI Technologies, you will be responsible for building and operating the data foundation that enables our LLM and ML research: from ingestion and augmentation, through labeling and quality control, to efficient data delivery for training and evaluation.
You will:
Own data pipelines for LLM training and evaluation
Design, build and maintain scalable pipelines to ingest, transform and serve large-scale text, log, code and semi-structured data from multiple products and internal systems.
Drive data augmentation and synthetic data generation
Implement and operate pipelines for data augmentation (e.g., prompt-based generation, paraphrasing, negative sampling, multi-positive pairs) in close collaboration with ML Research Engineers.
Build tagging, labeling and annotation workflows
Support human-in-the-loop labeling, active learning loops and semi-automated tagging. Work with domain experts to implement tools, schemas and processes for consistent, high-quality annotations.
Ensure data quality, observability and governance
Define and monitor data quality checks (coverage, drift, anomalies, duplicates, PII), manage dataset versions, and maintain clear documentation and lineage for training and evaluation datasets.
Optimize training data flows for efficiency and cost
Design storage layouts and access patterns that reduce training time and cost (e.g., sharding, caching, streaming). Work with ML engineers to make sure the right data arrives at the right place, in the right format.
Build and maintain data infrastructure for LLM workloads
Work with cloud and platform teams to develop robust, production-grade infrastructure: data lakes / warehouses, feature stores, vector stores, and high-throughput data services used by training jobs and offline evaluation.
Collaborate closely with ML Research Engineers and security experts
Translate modeling and security requirements into concrete data tasks: dataset design, splits, sampling strategies, and evaluation data construction for specific security use.

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from an office.

We are looking for a talented Data Engineer to help build and enhance the data platform that supports analytics, operations, and data-driven decision-making across the organization. You will work hands-on to develop scalable data pipelines, improve data models, ensure data quality, and contribute to the continuous evolution of our modern data ecosystem.

Youll collaborate closely with Senior Engineers, Analysts, Data Scientists, and stakeholders across the business to deliver reliable, well-structured, and well-governed data solutions.


What Youll Do:

Engineering & Delivery

Build, maintain, and optimize data pipelines for batch and streaming workloads.

Develop reliable data models and transformations to support analytics, reporting, and operational use cases.

Integrate new data sources, APIs, and event streams into the platform.

Implement data quality checks, testing, documentation, and monitoring.

Write clean, performant SQL and Python code.

Contribute to improving performance, scalability, and cost-efficiency across the data platform.

Collaboration & Teamwork

Work closely with senior engineers to implement architectural patterns and best practices.

Collaborate with analysts and data scientists to translate requirements into technical solutions.

Participate in code reviews, design discussions, and continuous improvement initiatives.

Help maintain clear documentation of data flows, models, and processes.

Platform & Process

Support the adoption and roll-out of new data tools, standards, and workflows.

Contribute to DataOps processes such as CI/CD, testing, and automation.

Assist in monitoring pipeline health and resolving data-related issues.

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from our office.

We are looking for a highly skilled Senior Data Engineer with strong architectural expertise to design and evolve our next-generation data platform. You will define the technical vision, build scalable and reliable data systems, and guide the long-term architecture that powers analytics, operational decision-making, and data-driven products across the organization.

This role is both strategic and hands-on. You will evaluate modern data technologies, define engineering best practices, and lead the implementation of robust, high-performance data solutions-including the design, build, and lifecycle management of data pipelines that support batch, streaming, and near-real-time workloads.

What Youll Do

Architecture & Strategy

Own the architecture of our data platform, ensuring scalability, performance, reliability, and security.
Define standards and best practices for data modeling, transformation, orchestration, governance, and lifecycle management.
Evaluate and integrate modern data technologies and frameworks that align with our long-term platform strategy.
Collaborate with engineering and product leadership to shape the technical roadmap.

Engineering & Delivery

Design, build, and manage scalable, resilient data pipelines for batch, streaming, and event-driven workloads.
Develop clean, high-quality data models and schemas to support analytics, BI, operational systems, and ML workflows.
Implement data quality, lineage, observability, and automated testing frameworks.
Build ingestion patterns for APIs, event streams, files, and third-party data sources.
Optimize compute, storage, and transformation layers for performance and cost efficiency.

Leadership & Collaboration

Serve as a senior technical leader and mentor within the data engineering team.
Lead architecture reviews, design discussions, and cross-team engineering initiatives.
Work closely with analysts, data scientists, software engineers, and product owners to define and deliver data solutions.
Communicate architectural decisions and trade-offs to technical and non-technical stakeholders.

We are looking for a Senior Data & Machine Learning Engineer to operate at the intersection of data platform engineering and machine learning enablement. This role is responsible for building scalable, efficient, and reliable data systems while enabling Data Science and Analytics teams to develop and deploy ML-driven features.

You will take ownership of the data and ML infrastructure layer, ensuring that pipelines, storage models, and compute usage are optimized, while also shaping how data workflows and ML solutions are designed across the organization.


Responsibilities
Data Platform & Infrastructure

Design, build, and maintain scalable data pipelines and storage systems supporting analytics and ML use cases
Ensure compute and cost efficiency across pipelines, storage models, and processing workflows
Own and improve data orchestration, transformation, and serving layers (e.g., Spark, DBT, streaming/batch systems)
Build and maintain shared infrastructure components, including:
IO managers and data access abstractions
Integrations with DBT, Spark, and other data frameworks
Internal tooling to improve developer productivity and reliability
ML Enablement & Collaboration

Partner closely with Data Science to design and productions ML solutions for new features and research initiatives
Translate experimental models into robust, scalable production systems
Support feature engineering, training pipelines, and inference workflows
Help define best practices for ML lifecycle management (training, validation, deployment, monitoring)
Data Quality, Governance & Best Practices

Enforce best practices for building and maintaining data processes across Data Analyst and Data Science teams
Define standards for:
Data modeling and transformations
Pipeline reliability and observability
Testing, versioning, and documentation
Improve data quality, consistency, and discoverability across the organization
Performance & Reliability

Optimize systems for performance, scalability, and cost efficiency
Monitor and troubleshoot data pipelines and ML systems in production
Implement observability (logging, metrics, alerting) across data workflows

We are looking for an experienced Data Engineering Team Leader.
In this role, you will lead and strengthen our Data Team, drive innovation, and ensure the robustness of our data and analytics platforms.
A day in the life and how youll make an impact:
Drive the technical strategy and roadmap for the data engineering function, ensuring alignment with overall business objectives.
Own the design, development, and evolution of scalable, high-performance data pipelines to enable diverse and growing business needs.
Establish and enforce a strong data governance framework, including comprehensive data quality standards, monitoring, and security protocols, taking full accountability for data integrity and reliability.
Lead the continuous enhancement and optimization of the data analytics platform and infrastructure, focusing on performance, scalability, and cost efficiency.
Champion the complete data lifecycle, from robust infrastructure and data ingestion to detailed analysis and automated reporting, to maximize the strategic value of data and drive business growth.

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 looking for an experienced and hands-on Data Engineer to lead the migration of enterprise data platforms to Google Cloud Platform (GCP).
In this role, you will design, build and maintain scalable ETL/ELT pipelines, develop advanced data models in BigQuery and contribute to the creation of a high-performance, reliable and cost-efficient data architecture.
You will work closely with analysts, data scientists and engineers and have real impact on how data is consumed across the organization.
What You Will Do:
Lead the migration of data from on-premise core systems to Google Cloud Platform (GCP).
Design and develop processed data layers (Silver and Gold) and data marts in BigQuery, including complex business logic.
Build, orchestrate and maintain data pipelines using Cloud Composer / Apache Airflow.
Develop robust data transformations, including cleansing, enrichment and data quality improvements.
Write efficient and optimized SQL queries in BigQuery with strong focus on performance and cost.
Create and maintain clear and up-to-date technical documentation for data architecture and processes.

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.
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 our next-generation data stack.
We believe three things matter for every role: 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 are looking for an experienced Full-Stack Engineer with strong backend expertise to join our growing Sales R&D team in Tel Aviv. This is a backend-leaning role focused on building and maintaining high-performance systems while contributing across full-stack services. As part of our team, youll drive the evolution of our Sales platform, take end-to-end ownership of impactful features, and contribute to an AI-first engineering culture that embraces automation, intelligent tooling, and continuous improvement. You will play a key role in improving product quality, engineering productivity, and system reliability as we support rapid business growth.
Responsibilities:
Design and deliver end-to-end features across backend services and frontend applications, with strong emphasis on backend architecture, data integrity, and system reliability.
Develop and maintain SPA web applications for admin and product platforms using React + TypeScript and Next.js, including contributing to the modernization of legacy Ruby components.
Develop and maintain backend services across Python (FastAPI) and Node.js (NestJS) microservices, ensuring strong performance, robust data management, and ongoing modernization efforts.
Design, implement, and maintain reliable REST APIs with clear contracts, validation, and consistent error handling.
Work with complex relational data models, continuously improving query performance and data reliability.
Lead investigations and root-cause analysis for performance, optimization, and production-related issues, driving long-term improvements.
Collaborate closely with frontend, backend, QA, and product teams in a multi-repo environment, including active code reviews and cross-site collaboration with our Boston engineering team.
Take ownership of code quality and production stability by writing automated tests (e.g., pytest, Jest) and driving issues from investigation through resolution and prevention.

we are looking for a Data Engineer.
As a 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 ore.
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.
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