The opportunity
You will lead a small, hands-on engineering team building the secure, scalable Core Analytics Data Access Platform that accelerates Datadog’s Applied AI and analytics capabilities. The team owns the Data Access Platform — a unified interface that lets AI and analytics teams…
What you'll do
Lead a Hands-On Engineering Team: Manage, mentor, and grow a small team of 2–4 data engineers (mix of senior and junior) across Paris and NYC, fostering technical excellence and career development.
Own Technical Direction and Delivery: Define architecture, engineering priorities, and the team roadmap for the Data Access Platform, driving implementation of scalable, secure data pipelines and platform services.
Contribute to Design and Code: Spend substantial time coding, reviewing, and shipping critical platform components to ensure performance, reliability, and operational excellence.
Partner with Internal Stakeholders: Work closely with Applied AI, Internal Product Analytics, product managers, and platform teams to define data contracts, APIs, SLAs, observability, and curated analytical datasets.
Ensure Data Security, Governance, and Reliability: Implement access controls, lineage, monitoring, and compliance guardrails to support safe model training and repeatable analytics workflows.
Plan and Scale the Platform: Evolve storage, processing frameworks, API patterns, and operational practices to enable broader adoption and prepare the platform for increased scope and usage.
What they're looking for
- Hands-On Engineering Manager who enjoys managing a small team while remaining: an active contributor to design and code.
- Experienced in Data Pipelines and At-Scale Data Engineering, with practical: experience building ETL/ELT, streaming and/or batch workflows, and production data servicing layers.
- Practical Knowledge of Our Core Stack: production experience with AWS, Spark, and Iceberg (or equivalent table formats and compute frameworks).
- Collaborative Partner to Data Scientists and Analysts: track record working with applied ML teams, data scientists, and analytics consumers to operationalize data for model training and analysis.
