Engineering Manager, Data Platform - adonis

Polluxa, Inc.; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Polluxa, Inc. is seeking a Data Platform Leader to guide a team of 4-6 engineers, owning the roadmap, delivery, and health. You’ll stay hands-on to review architecture and unblock hard problems when needed, in a lean-team setting.

This role requires deep experience with Snowflake, Python, AWS, and orchestration tools, and you’ll partner with DS/ML and customer-facing teams to scale reliable data platforms while controlling cost and governance.

Qualifications

• 8+ years in data or backend engineering.
• 2+ years directly managing engineers with ownership of outcomes.
• Hands-on capability today; can pass coding screen and discuss systems design.
• Experience operating production data platforms: warehouse architecture, orchestration, and transformation frameworks.
• Evidence of building or scaling a team and hiring.

Responsibilities

• Lead a team of 4-6 data platform engineers: performance, growth, coaching, and retention.
• Own the team roadmap with product and leadership for delivery against it.
• Set architecture direction with senior ICs and review designs.
• Own hiring: sourcing, interviewing, closing, and building the team for future needs.
• Run production: reliability, SLAs, on-call health, incidents, and cost governance.
• Partner with DS/ML, forward-deployed engineering, and customer teams; represent the platform to execs.
• Improve processes: planning cadence, reviews, and quality bars for a startup.
• Drive an AI-native culture; integrate AI tooling into workflows.

Skills

Data engineering

Backend engineering

People management

Delivery ownership

Systems design

Tools

Snowflake

Temporal

Airflow

dbt

SQLMesh

Python

AWS

Fivetran

Datadog

Job description

THE ROLE

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.

WHAT YOU WILL DO

• 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.

WHAT YOU WILL BRING

• 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.