לוח משרות דרושים ראש צוות דאטה משרה מלאה

We are building the next generation of digital heart-health products and our data platform is the foundation.
Were looking for a Data Platform Team Lead to own and evolve an AI-first, cloud-native data platform that already:
Serves 100+ data users across the company
Powers ML models impacting thousands of users every day
Supports production systems in a company that literally saves lives
You will lead a growing team of 4 data engineers and 1 BI developer, and work in close, day-to-day partnership with Product, Analytics, Data Science & Engineering
Our future is serving millions of users across multiple products in the heart-health ecosystem. This role owns the platform that will scale us there. We are intentionally building an AI-first / agentic data platform.
That means:
Automating table creation, validation, and testing
Agent-driven monitoring for data quality, freshness, and failures
Using agents to generate and maintain documentation
Reducing manual operational overhead so humans focus on architecture, leverage, and product impact
You will have full organizational support to rethink how a modern data platform should work in an AI-native environment, not incremental improvements, but fundamental design decisions.
Our Technologies stack: Python, Spark, Airflow, DBT, Kafka, AWS (Glue, EMR, S3, Athena and more), Snowflake, Docker, Kubernetes, MongoDB, Redis, Postgres, Elasticsearch, and evolving
Responsibilities:
Platform Leadership & Team Management: Lead, mentor, and grow a team of senior data engineers and BI developers. Set a high bar for technical quality, ownership, and delivery.
Core Data Platform Architecture: Own the design, evolution, and reliability of our cloud-native data platform, balancing scalability, cost, security, and developer velocity.
Deep Collaboration with R&D: Work closely with product, engineering, and ML teams to ensure the data platform enables fast experimentation, production ML, and new product development.
Production-Grade Data Systems: Oversee end-to-end data pipelines, streaming and batch processing, semantic layers, and analytics foundations that serve the entire organization.
Operational Excellence: Ensure data quality, freshness, observability, and incident response meet the standards of a mission-critical system.

It starts with you - a technical leader whos passionate about data pipelines, data modeling, and growing high-performing teams. You care about data quality, business logic correctness, and delivering trusted data products to analysts, data scientists, and AI systems. Youll lead the Data Engineering team in building ETL/ELT pipelines, dimensional models, and quality frameworks that turn raw data into actionable intelligence.
If you want to lead a team that delivers the data products powering mission-critical AI systems, join mission - this role is for you.
:Responsibilities
Lead and grow the Data Engineering team - hiring, mentoring, and developing engineers while fostering a culture of ownership and data quality.
Define the data modeling strategy - dimensional models, data marts, and semantic layers that serve analytics, reporting, and ML use cases.
Own ETL/ELT pipeline development using platform tooling - orchestrated workflows that extract from sources, apply business logic, and load into analytical stores.
Drive data quality as a first-class concern - validation frameworks, testing, anomaly detection, and SLAs for data freshness and accuracy.
Establish lineage and documentation practices - ensuring consumers understand data origins, transformations, and trustworthiness.
Partner with stakeholders to understand data requirements and translate them into well-designed data products.
Build and maintain data contracts with consumers - clear interfaces, versioning, and change management.
Collaborate with Data Platform to define requirements for new platform capabilities; work with Datastores on database needs; partner with ML, Data Science, Analytics, Engineering, and Product teams to deliver trusted data.
Design retrieval-friendly data products - RAG-ready paths, feature tables, and embedding pipelines - while maintaining freshness and governance SLAs.