Sr. Product Manager, Lakeflow
DatabricksAmsterdam, NetherlandsPosted 24 February 2026
Job Description
RDQ426R402
At Databricks, we are passionate about enabling data teams to solve the world’s toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world’s best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.
As a Product Manager on the Lakeflow Jobs team , you will shape the future of data-aware orchestration on the Databricks Data Intelligence Platform. Jobs is Databricks’ built-in orchestrator, managing hundreds of millions of workloads every week with 99.95% reliability. It powers ETL, AI/ML, BI, and streaming workloads for thousands of customers. Your focus will be on evolving control-flow capabilities, authoring experiences, and observability features that allow customers to productionize workflows with best practices like source control, code promotion, and system observability.
This role requires a deep understanding of orchestration workflows , data pipelines , and service reliability. You will work closely with engineers, designers, field teams, and customers to define the roadmap, drive adoption, and ensure Jobs is the best-in-class orchestration platform for modern data and AI teams.
The impact you’ll have:
Define strategy roadmap: Set vision and priorities across orchestration, triggers, authoring, and observability. Translate customer needs into features and integrations that accelerate Lakeflow Jobs adoption.
Drive metrics observability: Define success metrics for adoption, engagement, and developer efficiency. Use data to guide roadmap decisions and refine the product experience.
Collaborate cross-functionally: Partner with global engineering, field engineering, solution architects, and external partners to deliver impactful features and integrations.
Champion customer needs: Talk to hundreds of customers, understand their journeys, and advocate for features that reduce friction in authoring, monitoring, and orchestrating workloads.
Own the product lifecycle: Write PRDs, release notes, and documentation, and drive launches that ensure customer and business success.
What we look for:
5+ yrs of experience
Bachelors degree in Computer Science or similar field
Experience in Product Management (inbound, outbound, cross-functional processes)
Solid understanding of major orchestration tools (Airflow, Dagster, Prefect etc.)
Technical skills understanding of data pipelines (Spark, dbt, Lakeflow Pipelines)
Strong data analysis and operationalization skills (SQL, Python, building operational dashboards)
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on
Twitter , LinkedIn
and
Facebook .
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit https://www.mybenefitsnow.com/databricks .
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Datab ... (truncated, view full listing at source)
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