Data Platform Engineering Manager

Engineering Manager, Data Platform

Jahnel Group's mission is to provide the absolute best environment for software creators to pursue their passion by connecting them with great clients doing meaningful work. This position is with one of our closest clients, where you'll play a key role in building and scaling a reliable, high-performance data platform that supports healthcare operations and enables data-driven decision-making.

Who We're Looking For

We're looking for an Engineering Manager, Data Platform who can provide technical leadership while managing a team of data platform engineers. This role combines hands-on engineering, team development, and strategic execution, with ownership of the platform's roadmap, architecture, delivery, and operational health.

You'll work closely with senior engineers, Product, and the Head of Data to translate business needs into technical priorities, guide architectural decisions, and ensure the team delivers reliable, scalable solutions. You'll also collaborate with Data Science and Machine Learning, forward-deployed engineering, and customer-facing teams to support evolving platform requirements.

This is a hands-on leadership role in a lean team environment. You'll be expected to contribute directly to the codebase and data pipelines, review architecture, resolve complex technical challenges, and help establish engineering practices that support continued growth. You'll also drive improvements in platform reliability, performance, cost management, and governance while fostering an AI-native engineering culture.

The ideal candidate has experience building and operating production data platforms, managing and developing engineering teams, and balancing technical execution with people leadership. They can make sound architectural decisions, maintain strong relationships across the organization, and adapt to changing priorities without losing sight of long-term platform goals.

The ideal candidate:

• Has 8+ years of experience in data engineering, backend engineering, or related technical fields
• Has 2+ years of direct engineering management experience, including performance management, hiring, and team development
• Maintains strong hands-on engineering skills and can contribute directly to code, pipelines, and technical problem-solving
• Has experience building and operating production data platforms using Snowflake or similar data warehouse technologies
• Understands data orchestration tools such as Temporal or Airflow and transformation frameworks such as SQLMesh or dbt
• Can define technical direction, evaluate architectural tradeoffs, and guide senior engineers through complex design decisions
• Has successfully built, scaled, or developed engineering teams and can balance team performance with individual growth
• Can translate ambiguous business requirements into clear technical plans, priorities, and delivery commitments
• Understands production reliability, service-level objectives, incident response, on-call practices, and infrastructure cost management
• Can collaborate effectively with Product, Data Science, Machine Learning, and customer-facing teams
• Is comfortable working in a startup environment where priorities evolve and leaders remain close to the technical work
• Can introduce processes and engineering standards that improve delivery without creating unnecessary overhead
• Is interested in integrating AI development tools, including Claude Code and similar technologies, into engineering workflows
• Communicates technical decisions, tradeoffs, and risks clearly to both technical and non-technical stakeholders
• Values collaboration, welcomes different perspectives, and can build alignment across teams

Responsibilities

• Manage and develop a team of 4–6 data platform engineers, supporting performance, professional growth, coaching, and retention
• Own the data platform team's roadmap in partnership with Product and the Head of Data
• Translate business needs and ambiguous requirements into prioritized technical plans, resource allocation, and delivery commitments
• Establish architectural direction in collaboration with senior individual contributors
• Review technical designs, evaluate tradeoffs, and guide decisions around scalability, reliability, maintainability, and cost
• Stay hands-on with the codebase and data pipelines, particularly during onboarding and when complex technical challenges require direct involvement
• Lead hiring efforts, including sourcing, interviewing, evaluating candidates, and building the team for future platform needs
• Oversee production reliability, service-level agreements, on-call practices, incident response, and operational readiness
• Monitor and optimize data platform performance, warehouse costs, and resource utilization
• Guide the development and maintenance of data ingestion, transformation, orchestration, and modeling infrastructure
• Support enterprise healthcare data integrations, including EHR extraction infrastructure and sources such as Epic Clarity, Athena, and ODBC-based systems
• Drive core entity modeling and platform readiness to support growing hospital and health-system requirements
• Establish engineering practices that improve code quality, system reliability, observability, and delivery consistency
• Partner with Data Science and Machine Learning, forward-deployed engineering, and customer-facing teams to support platform needs and evolving requirements
• Represent the data platform team to executive stakeholders and, when needed, enterprise customers
• Build effective planning, design review, and delivery processes appropriate for a lean, growing organization
• Identify opportunities to simplify workflows, remove unnecessary process, and improve team effectiveness
• Promote an AI-native engineering culture by integrating tools such as Claude Code into development, code review, and operational workflows
• Establish expectations for effective and responsible use of AI-assisted engineering tools
• Communicate platform priorities, technical decisions, risks, and progress to relevant stakeholders
• Balance immediate delivery needs with long-term platform scalability, reliability, governance, and maintainability

Some Must-Haves

• 8+ years of experience in data engineering, backend engineering, or a related technical discipline
• 2+ years of direct experience managing engineers, including ownership of delivery, performance management, and hiring
• Strong current hands-on engineering capabilities, including the ability to pass a coding assessment and participate in systems design discussions
• Experience operating production data platforms, including data warehouse architecture, orchestration, and transformation frameworks
• Experience with Snowflake or a comparable cloud data warehouse
• Familiarity with Temporal, Airflow, or similar orchestration technologies
• Experience with SQLMesh, dbt, or similar data transformation frameworks
• Proven ability to guide architecture decisions and resolve complex engineering challenges
• Experience hiring, coaching, and developing engineers, including making difficult personnel decisions when necessary
• Strong understanding of production operations, including reliability, incident response, monitoring, and cost optimization
• Ability to translate business requirements into technical roadmaps and actionable delivery plans
• Strong stakeholder management skills and the ability to communicate technical tradeoffs and push back constructively
• Ability to balance people leadership, technical direction, and hands-on execution
• Comfort working in a lean team with evolving priorities and a high degree of ownership
• Strong communication and collaboration skills across engineering, product, and business teams

Technical Requirements

• Data Platform Engineering
• Data Engineering
• Backend Engineering
• Snowflake
• SQL
• SQLMesh
• Data Transformation & Modeling
• Temporal or Airflow
• Python
• AWS
• Fivetran
• Data Ingestion & Integration
• Enterprise EHR Data Extraction
• Epic Clarity
• Athena
• ODBC-Based Data Sources
• Data Warehouse Architecture
• Production Data Pipelines
• Data Platform Reliability & Observability
• Datadog
• Incident Response & On-Call Operations
• Cloud Infrastructure & Cost Optimization
• Systems Design & Architecture
• Engineering Management & Technical Leadership
• AI-Assisted Engineering Tools

Nice-to-Haves

• Experience with SQLMesh specifically
• Experience with Temporal specifically
• Experience building and scaling enterprise healthcare data platforms
• Familiarity with EHR systems, including Epic Clarity and Athena
• Experience supporting hospital and health-system integrations
• Experience with Fivetran for data ingestion
• Experience with Datadog for monitoring and observability
• Experience improving data warehouse performance and controlling cloud infrastructure costs