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Data Platform Manager

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Onsite

Data Platform Manager

Harvey Nash

Data Platform Manager

Skills

Data EngineeringPythonData ScienceSnowflakeDatadogMachine LearningAIAWSApache AirflowPerformance Management

6 days ago

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Important information


Contract type:

Permanent

Salary:

$220,000 – $260,000 / year

Location:

New York, NY, USA

Work mode:

Onsite

Published on:

16 October 2026

What they need


Data Platform Manager

Location: New York, NY (Hybrid - 3 Days Onsite)

Job Type: Full-Time

Position Overview

We are seeking a highly motivated Data Platform Manager to lead and grow a team of data platform engineers while remaining hands-on with architecture, development, and platform operations. This role is ideal for a technical leader who enjoys balancing people management, strategic planning, and direct technical contribution in a fast-paced, startup-oriented environment.

As a player-coach, you will own the team's roadmap, delivery, reliability, and overall health while helping shape the future of our enterprise data platform. During your first few months, you should expect to work directly within the codebase, pipelines, and infrastructure to build technical context and establish credibility with the team.

The platform leverages Snowflake, SQLMesh, Temporal, Python, AWS, Fivetran, and Datadog to support enterprise healthcare data integrations, EHR extract infrastructure, core data modeling, governance, scalability, reliability, and cost optimization.

Key Responsibilities

Leadership & Team Management

  • Lead, mentor, and develop a team of 4-6 Data Platform Engineers.
  • Conduct performance management, career development, coaching, and retention activities.
  • Foster a culture of accountability, technical excellence, and continuous improvement.
  • Lead recruiting efforts, including sourcing, interviewing, and hiring top engineering talent.

Platform Strategy & Delivery

  • Own and drive the data platform roadmap in partnership with Product and Data leadership.
  • Translate complex business requirements into actionable technical initiatives and delivery plans.
  • Ensure successful execution of projects while maintaining high quality and reliability standards.
  • Establish scalable engineering processes, planning cadences, and review practices appropriate for a high-growth environment.

Technical Leadership

  • Provide architecture guidance and technical direction across data platform initiatives.
  • Review designs, evaluate trade-offs, and make critical technical decisions.
  • Remain actively involved in development, troubleshooting, and solution implementation when needed.
  • Guide platform architecture across data warehousing, orchestration, transformation, and observability.

Operational Excellence

  • Own platform reliability, scalability, performance, and cost optimization.
  • Manage production operations, SLAs, incident response, and on-call processes.
  • Drive improvements in monitoring, alerting, observability, and platform governance.
  • Ensure data platform readiness for large-scale healthcare and enterprise deployments.

Cross-Functional Collaboration

  • Partner with Data Science, Machine Learning, Engineering, and Customer-Facing teams.
  • Communicate platform priorities, risks, and outcomes to executives and stakeholders.
  • Support enterprise customer engagements when technical leadership is required.
  • Promote an AI-driven engineering culture through adoption of modern development tools and best practices.

Required Qualifications

  • 8+ years of experience in Data Engineering, Backend Engineering, or Data Platform Development.
  • 2+ years of direct people management experience with ownership of hiring, performance management, and team delivery.
  • Strong hands-on coding ability with proficiency in Python and modern data engineering practices.
  • Experience designing and operating production-scale data platforms.
  • Expertise with Snowflake or comparable cloud data warehouse technologies.
  • Experience with workflow orchestration platforms such as Temporal , Airflow , or similar tools.
  • Hands-on experience with SQLMesh, dbt , or related transformation frameworks.
  • Strong understanding of data architecture, pipeline design, reliability engineering, and system scalability.
  • Experience leading technical discussions, architecture reviews, and system design initiatives.
  • Ability to manage competing priorities and operate effectively in fast-moving startup environments.

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