Manager I, Engineering - Core Analytics

Datadog Datadog · Enterprise · New York, NY · Leadership

Manager I, Engineering - Core Analytics at Datadog leads a team building a secure, scalable Data Access Platform for Applied AI and analytics. The role involves technical direction, coding, and partnering with AI/analytics teams to provide production-ready datasets and APIs for model training and analysis, balancing leadership with platform planning.

What you'd actually do

  1. 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.
  2. 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.
  3. Contribute to Design and Code: Spend substantial time coding, reviewing, and shipping critical platform components to ensure performance, reliability, and operational excellence.
  4. 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.
  5. Ensure Data Security, Governance, and Reliability: Implement access controls, lineage, monitoring, and compliance guardrails to support safe model training and repeatable analytics workflows.

Skills

Required

  • Data Engineering
  • ETL/ELT
  • streaming and/or batch workflows
  • production data servicing layers
  • AWS
  • Spark
  • Iceberg
  • SLAs
  • observability
  • incident response
  • data governance

Nice to have

  • managing a small team
  • applied ML teams
  • data scientists
  • analytics consumers

What the JD emphasized

  • secure, scalable
  • production-ready datasets
  • model training
  • analysis
  • data pipelines
  • platform services
  • performance, reliability, and operational excellence
  • data contracts, APIs, SLAs, observability
  • access controls, lineage, monitoring, and compliance guardrails
  • safe model training
  • repeatable analytics workflows
  • storage, processing frameworks, API patterns, and operational practices
  • broader adoption
  • prepare the platform for increased scope and usage

Other signals

  • Data Access Platform
  • Applied AI
  • model training
  • analytics capabilities
  • production-ready datasets
  • APIs for model training
  • scalable, secure data pipelines