דרושים » דאטה » (OS) Senior data Engineer

Were looking for an experienced and passionate Staff Data Engineer to join our Data Platform group in TLV as a Tech Lead. As the Groups Tech Lead, youll shape and implement the technical vision and architecture while staying hands-on across three specialized teams: Data Engineering Infra, Machine Learning Platform, and Data Warehouse Engineering, forming the backbone of our companys data ecosystem.
The groups mission is to build a state-of-the-art Data Platform that drives our company toward becoming the most precise and efficient insurance company on the planet. By embracing Data Mesh principles, we create tools that empower teams to own their data while leveraging a robust, self-serve data infrastructure. This approach enables Data Scientists, Analysts, Backend Engineers, and other stakeholders to seamlessly access, analyze, and innovate with reliable, well-modeled, and queryable data, at scale.
In this role youll :
Technically lead the group by shaping the architecture, guiding design decisions, and ensuring the technical excellence of the Data Platforms three teams
Design and implement data solutions that address both applicative needs and data analysis requirements, creating scalable and efficient access to actionable insights
Drive initiatives in Data Engineering Infra, including building robust ingestion layers, managing streaming ETLs, and guaranteeing data quality, compliance, and platform performance
Develop and maintain the Data Warehouse, integrating data from various sources for optimized querying, analysis, and persistence, supporting informed decision-makingLeverage data modeling and transformations to structure, cleanse, and integrate data, enabling efficient retrieval and strategic insights
Build and enhance the Machine Learning Platform, delivering infrastructure and tools that streamline the work of Data Scientists, enabling them to focus on developing models while benefiting from automation for production deployment, maintenance, and improvements. Support cutting-edge use cases like feature stores, real-time models, point-in-time (PIT) data retrieval, and telematics-based solutions
Collaborate closely with other Staff Engineers across our company to align on cross-organizational initiatives and technical strategies
Work seamlessly with Data Engineers, Data Scientists, Analysts, Backend Engineers, and Product Managers to deliver impactful solutions
Share knowledge, mentor team members, and champion engineering standards and technical excellence across the organization.

We are seeking a highly motivated and experienced BI & Data Engineer to join our fast-growing Data team. Reporting to our Development Team Leader. You will be supporting the team on all Data, pipelines and reports. Help turn raw data into business insights. Managing and designing BI solutions, including ETL processes, Data modeling and reporting. Our BI & data Developer would also enjoy our future data technological stack like: Airflow, DBT, Kafka streaming, AWS/Azure, Python, Advanced ETL tools and more.
Responsibilities:
Gathering requirements from internal customers and designing and planning BI solutions.
Develop and maintain ETL/ELT pipelines using Airflow for orchestration and DBT for transformation
Design and optimize data models, ensuring performance, scalability, and cost efficiency.
Collaborate with BI developers, analysts, AI agents, and product teams to deliver reliable datasets for reporting and advanced analytics.
Development in various BI and big data tools according to R&D methodologies and best practices
Maintain and manage production platforms.

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 a Data Engineer to join our growing team!
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
In this role, you will be responsible for:
Designing, building, and maintaining scalable data pipeline architectures
Developing ETL processes to integrate data from multiple sources
Creating and optimizing data models for efficient storage and retrieval
Implementing data quality controls and monitoring systems
Collaborating with data scientists and analysts to deliver data solutions
Building and maintaining data warehouses and data lakes
Performing in-depth data analysis and providing insights to stakeholders
Taking full ownership of data quality, documentation, and governance processes
Building and maintaining comprehensive reports and dashboards
Ensuring data security and regulatory compliance.

We are seeking a talented Data Engineer to join our BI & Data team in Tel Aviv. You will play a pivotal role in building and optimizing the data infrastructure that powers our business. In this mid-level position, your primary focus will be on developing a robust single source of truth (SSOT) for revenue data, along with scalable data pipelines and reliable orchestration processes. If you are passionate about crafting efficient data solutions and ensuring data accuracy for decision-making, this role is for you.



Responsibilities:

Pipeline Development & Integration

- Design, build, and maintain robust data pipelines that aggregate data from various core systems into our data warehouse (BigQuery/Athena), with a special focus on our revenue Single Source of Truth (SSOT).

- Integrate new data sources (e.g. advertising platforms, content syndication feeds, financial systems) into the ETL/ELT workflow, ensuring seamless data flow and consolidation.

- Implement automated solutions for ingesting third-party data (leveraging tools like Rivery and scripts) to streamline data onboarding and reduce manual effort.

- Leverage AI-assisted development tools (e.g., Cursor, GitHub Copilot) to accelerate pipeline development

Optimization & Reliability

- Optimize ETL processes and SQL queries for performance and cost-efficiency - for example, refactoring and cleaning pipeline code to reduce runtime and cloud processing costs.

- Develop modular, reusable code frameworks and templates for common data tasks (e.g., ingestion patterns, error handling) to accelerate future development and minimize technical debt.

- Orchestrate and schedule data workflows to run reliably (e.g. consolidating daily jobs, setting up dependent task flows) so that critical datasets are refreshed on time.

- Monitor pipeline execution and data quality on a daily basis, quickly troubleshooting issues or data discrepancies to maintain high uptime and trust in the data.

Collaboration & Documentation

- Work closely with analysts and business stakeholders to understand data requirements and ensure the infrastructure meets evolving analytics needs (such as incorporating new revenue streams or content cost metrics into the SSOT).

- Document the data architecture, pipeline processes, and data schemas in a clear way so that the data ecosystem is well-understood across the team.

- Continuously research and recommend improvements or new technologies (e.g. leveraging AI tools for data mapping or anomaly detection) to enhance our data platforms capabilities and reliability and ensure our data ecosystem remains a competitive advantage.

Required Senior Data Engineer
About us:
A pioneering health-tech startup on a mission to revolutionize weight loss and well-being. Our innovative metabolic measurement device provides users with a comprehensive understanding of their metabolism, empowering them with personalized, data-driven insights to make informed lifestyle choices.
Data is at the core of everything we do. We collect and analyze vast amounts of user data from our device and app to provide personalized recommendations, enhance our product, and drive advancements in metabolic health research. As we continue to scale, our data infrastructure is crucial to our success and our ability to empower our users.
About the Role:
As a Senior Data Engineer, youll be more than just a coder - youll be the architect of our data ecosystem. Were looking for someone who can design scalable, future-proof data pipelines and connect the dots between DevOps, backend engineers, data scientists, and analysts.
Youll lead the design, build, and optimization of our data infrastructure, from real-time ingestion to supporting machine learning operations. Every choice you make will be data-driven and cost-conscious, ensuring efficiency and impact across the company.
Beyond engineering, youll be a strategic partner and problem-solver, sometimes diving into advanced analysis or data science tasks. Your work will directly shape how we deliver innovative solutions and support our growth at scale.
Responsibilities:
Design and Build Data Pipelines: Architect, build, and maintain our end-to-end data pipeline infrastructure to ensure it is scalable, reliable, and efficient.
Optimize Data Infrastructure: Manage and improve the performance and cost-effectiveness of our data systems, with a specific focus on optimizing pipelines and usage within our Snowflake data warehouse. This includes implementing FinOps best practices to monitor, analyze, and control our data-related cloud costs.
Enable Machine Learning Operations (MLOps): Develop the foundational infrastructure to streamline the deployment, management, and monitoring of our machine learning models.
Support Data Quality: Optimize ETL processes to handle large volumes of data while ensuring data quality and integrity across all our data sources.
Collaborate and Support: Work closely with data analysts and data scientists to support complex analysis, build robust data models, and contribute to the development of data governance policies.

we are seeking a Senior Data Infra Engineer. You will be responsible for designing and building all data, ML pipelines, data tools, and cloud infrastructure required to transform massive, fragmented data into a format that supports processes and standards. Your work directly empowers business stakeholders to gain comprehensive visibility, automate key processes, and drive strategic impact across the company.
Responsibilities
Design and Build Data Infrastructure: Design, plan, and build all aspects of the platform's data, ML pipelines, and supporting infrastructure.
Optimize Cloud Data Lake: Build and optimize an AWS-based Data Lake using cloud architecture best practices for partitioning, metadata management, and security to support enterprise-scale operations.
Lead Project Delivery: Lead end-to-end data projects from initial infrastructure design through to production monitoring and optimization.
Solve Integration Challenges: Implement optimal ETL/ELT patterns and query techniques to solve challenging data integration problems sourced from structured and unstructured data.

Are you a talented and experienced Data Engineer? If so, we want you to be part of our dynamic Data Engineering Team, a part of the R&D, contributing to our vision and making a difference in the eCommerce landscape. Join us on this journey as we seek the best and brightest minds to drive our mission forward.

Responsibilities:
Developing, implementing and supporting robust, scalable solutions to improve business analysis capabilities.
Managing data pipelines from multiple sources, including designing, implementing, and maintaining.
Translating business priorities into data models by working with business analysts and product analysts.
Collaborate across the business with various stakeholders, such as data developers, systems analysts, data scientists and software engineers.
Owning the entire data development process, including business knowledge, methodology, quality assurance, and maintenance.
Work independently while considering all functional and non-functional aspects and provide high quality and robust infrastructures to the organization.