Data Platform Manager

Harvey Nash; New York (NY); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Harvey Nash data platform team in New York seeks a Data Platform Manager to lead a player-coach team owning the roadmap, delivery, and health of its data platform stack.

You will guide architecture, unblock hard problems, and contribute directly when needed, with a focus on Snowflake, SQLMesh, Temporal, Python, and AWS. Expect collaboration with enterprise data users and executives in a fast-growing environment.

Qualifications

• 8+ years in data or backend engineering.
• 2+ years directly managing engineers with clear ownership of outcomes (delivery, performance management, hiring).
• Genuine hands-on capability today, you can pass our coding screen and discuss systems design.
• 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 hard people calls you can discuss honestly.
• Stakeholder range: you manage up with judgment, push back with business framing, and keep relationships when you say no.
• Startup metabolism: comfortable with lean teams, shifting priorities, and being close to the work.

Responsibilities

• Manage a team of 4-6 data platform engineers: performance, growth, coaching, and retention.
• Own the team roadmap with product and 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, trade-off conversations, and know when to overrule or 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.
• Drive an AI-native engineering culture. Integrate AI development tooling (Claude Code and similar) into how the team builds, reviews, and operates.

Skills

Team leadership

Data platform engineering

Architecture review

Snowflake

Python

AWS

SQLMesh

Temporal

Tools

Snowflake

SQLMesh

Temporal

Airflow

dbt

Fivetran

Datadog

Python

AWS

Job description

Job Title: Data Platform Manager
Location: New York, NY (3 Days onsite)
Duration: Full-time

Job Description

You will lead a Data Platformteam as a player-coach: owning the team’s roadmap, delivery, and health whilestaying technically credible enough to review architecture, unblock hardproblems, and contribute directly when the situation calls for it.
The team operates on Snowflake, SQLMesh, Temporal, Python, and AWS, withFivetran for ingestion and Datadog for observability. The mandate spansenterprise EHR extract infrastructure (Epic Clarity, Athena, ODBC-basedsources), core entity modeling, hospital health-system readiness, and platformreliability, 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 ofData. Turn ambiguous business needs into a sequenced, staffed plan and beaccountable for delivery against it.
• Set architecture direction with your senior ICs.Review designs, force the right trade-off conversations, and know when tooverrule and when to defer.
• Own hiring: sourcing, interviewing, closing, andraising the bar. Build the team the platform needs two quarters from now.
• Run production: reliability, SLAs, on-callhealth, 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 theplatform to executives and, when needed, to enterprise customers.
• Build process where it creates leverage and killit where it does not: planning cadence, review practices, and quality barsappropriate to a startup, not a big company.
• Drive an AI-native engineering culture. IntegrateAI 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 ofoutcomes (delivery, performance management, hiring).
• Genuine hands-on capability today, nothistorically. You can pass our coding screen and hold your own in asystems design discussion about warehouses, orchestration, and pipelinefailure modes.
• Direct experience operating production dataplatforms: warehouse architecture (Snowflake or similar), orchestration(Temporal, Airflow, or similar), and transformation frameworks(SQLMesh/dbt).
• Evidence of building or scaling a team: hiredwell, grew people, and made at least one hard people call you can talkabout honestly.
• Stakeholder range: you manage up with judgment,push back with business framing, and keep relationships intact when yousay no.
• Startup metabolism: comfortable with lean teams,shifting priorities, and being close to the work.