Lead Data Engineer - Identity

The Opportunity

Identity is central to how AdTech works today: advertisers want cross-surface reach, user-level measurement, and lower-funnel attribution. Their data reaches us two ways, and each has a clear next step: direct onboarding currently runs on a partner's identity spine and we want to build a new, multi-source graph of our own; DMP feeds are proven and now need to scale as we enter in-app inventory.

In this role you'll own the graph, a standardized ingestion path that stands up each new feed faster and cheaper than the last, and the audience state and reporting behind self-serve discovery. You'll also set the technical bar for the engineers building it with you.

The Daily To-Do

• Build a new identity graph. Take stock of what we have today, set its direction, and sequence the rollout: identifier sync, translation, clustering (with Data Science), opt-out handling.
• Standardize partner and client onboarding across web, CTV and mobile identifiers, including cleanroom onboarding, so each new feed costs less to stand up than the last.
• Ready the identity audience data layer for self-serve: creation, activation, state, and the reporting clients will discover audiences through.
• Own and raise the bar on the domain's observability and alert response. Inventory today's signals, monitors and alerts, centralize them, and bring each to standard: a freshness and quality commitment, context for AI-assisted triage, and a runbook.
• Lead and grow the domain's data engineers. Define the standards for testability, cost efficiency and the patterns worth repeating, then raise the team to them through your own code, reviews, and knowledge-sharing, recording decisions in ADRs.

Qualifications

• You've designed and owned large-scale, interdependent data systems, including at least one you built from scratch, and you turn ambiguity into a sequenced roadmap with Product and Data Partnerships.
• You've led engineers, setting direction, reviewing work, developing people, while staying hands-on.
• You have mastery of Python, Airflow and Spark, and write transformations that are idiomatic, testable and tuned for cost and performance; you write SQL for Snowflake with the same discipline.
• You're at home in AWS and Kubernetes, can read infrastructure logs to diagnose failures and slowness, and have worked with third-party APIs inside ingestion pipelines.
• You're fluent with AI tooling in your own work, and you think about what makes a codebase legible to it.

Strongly Preferred

• Identity resolution or graph work in AdTech: matching, device and household graphs.
• Privacy and consent obligations: opt-outs, deletion, GDPR and CCPA.
• Data cleanrooms for partner or client onboarding.
• CI/CD with GitHub Actions/ArgoCD; monitoring with VictoriaMetrics/Prometheus/Grafana.

Nice To Have

• Iceberg or a comparable table format at production scale.
• Streaming or near-real-time processing (Kafka, Redpanda or similar).
• Low-latency stores such as Aerospike
• Experience with OLAP databases like Clickhouse.