Engineering Manager, Data Platform

Adonis•New York, NY

1d•$225,000 - $260,000•Onsite

About The Position

You will lead a Data Platform team as a player-coach: owning the team’s roadmap, delivery, and health while staying technically credible enough to review architecture, unblock hard problems, and contribute directly when the situation calls for it. This is a lean-team environment - you cannot operate as a pure management layer. In your first 30 days you should expect to be hands-on in the codebase and pipelines while you build context. The team operates on Snowflake, SQLMesh, Temporal, Python, and AWS, with Fivetran for ingestion and Datadog for observability. The mandate spans enterprise EHR extract infrastructure (Epic Clarity, Athena, ODBC-based sources), core entity modeling, hospital health-system readiness, and platform reliability, cost, and governance at growing scale.

Requirements

8+ years in data or backend engineering, including 2+ years directly managing engineers with clear ownership of outcomes (delivery, performance management, hiring).
Genuine hands-on capability today, not historically. You can pass our coding screen and hold your own in a systems design discussion about warehouses, orchestration, and pipeline failure modes.
Direct experience operating production data platforms: warehouse architecture (Snowflake or similar), orchestration (Temporal, Airflow, or similar), and transformation frameworks (SQLMesh/dbt).
Evidence of building or scaling a team: hired well, grew people, and made at least one hard people call you can talk about honestly.
Stakeholder range: you manage up with judgment, push back with business framing, and keep relationships intact when you say no.
Startup metabolism: comfortable with lean teams, shifting priorities, and being close to the work.

Nice To Haves

Healthcare data experience: EHR integrations, claims (837/835), HL7/FHIR, PHI/HIPAA operating constraints.
Experience owning warehouse cost management, RBAC/governance, or vendor relationships (Snowflake, Fivetran).
Prior player-coach roles at Series A–C companies.

Responsibilities

Manage a team of 4–6 data platform engineers: performance, growth, coaching, and retention.
Own the team roadmap with product and the Head of Data. Turn ambiguous business needs into a sequenced, staffed plan and be accountable for delivery against it.
Set architecture direction with your senior ICs. Review designs, force the right trade-off conversations, and know when to overrule and when to defer.
Own hiring: sourcing, interviewing, closing, and raising the bar. Build the team the platform needs two quarters from now.
Run production: reliability, SLAs, on-call health, incident response, and warehouse cost as a first-class metric.
Operate across the org. Partner with DS/ML, forward-deployed engineering, and customer-facing teams; represent the platform to executives and, when needed, to enterprise customers.
Build process where it creates leverage and kill it where it does not: planning cadence, review practices, and quality bars appropriate to a startup, not a big company.
Drive an AI-native engineering culture. Integrate AI development tooling (Claude Code and similar) into how the team builds, reviews, and operates, and hold a bar for using it well.