Head of Data / Staff Data Engineer

About the role

You'll own Manifest's data platform — its architecture, reliability, and direction — and lead the team that builds it. The platform works, but scaling to the next order of magnitude requires deliberate architectural decisions: case data spans two generations of systems, identity resolution stitches together five sources per person, and the warehouse now needs to power AI product features, not just dashboards. You'll define what this platform looks like in two years, sequence a path to get there without a full rewrite, and keep the data trustworthy throughout. You'll report to the Director of Engineering and partner closely with Engineering, Marketing, Sales, and Legal Operations.

Responsibilities

• Own the target-state data architecture — ingestion, modeling, semantic layer, and serving — with a sequenced roadmap and named trade-offs
• Drive build-vs-buy and tooling decisions across ingestion, transformation, orchestration, and observability
• Own data trust: make freshness, correctness, and lineage observable; enforce standards in CI
• Maintain a single definitional layer so one metric means the same thing everywhere — BigQuery, HubSpot, board deck
• Build governed foundations for AI features, including identity resolution and canonical entity models
• Lead and grow the team while staying close enough to the code to review hard designs and complex queries

Requirements

• 8+ years in data engineering, including serving as architect — not just contributor — of a cloud data platform
• Deep hands-on fluency with BigQuery (or Snowflake/Databricks), dbt, Python, and advanced SQL
• Real production experience: your opinions on orchestration, idempotency, and testing came from being paged
• Experience leading engineers formally or as a technical lead, with comfort in a player-coach role
• Treats access control, PII handling, and auditability as core design requirements, not afterthoughts

Qualifications

• Marketing attribution or multi-touch modeling across CRM, product, and clickstream data
• Experience building data foundations for ML or LLM-based product features
• Background in legal, healthcare, or fintech; early data hire experience at a high-growth company
• You'd rather improve something real and working than build from an empty repo
• You want your architecture to be load-bearing — decisions here affect ad spend,