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 discover and self-serve production-ready datasets while abstracting underlying systems and embedding required legal and compliance guardrails. In this role you’ll own technical direction, contribute to design and code, and partner closely with Applied AI, Product Analytics, and internal platform teams to provide reliable datasets and APIs for model training and analysis. This role balances day-to-day engineering leadership with long-term platform planning.
At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.
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.
Who You Are:
- 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.
- Operationally Strong: experience defi