Manager, Engineering (Data Platform)

Garner Health; New York (NY); Hybrid; USD 310,000 - 385,000; Full time

Job summary

Garner Health, based in New York City, is seeking an execution-focused Data Engineering Manager to lead the Data Platform Team. You will drive end-to-end design, implement high-throughput data infrastructure, and champion an AI-native development philosophy in a fast-paced startup environment.

The role requires 5+ years in data engineering and 2+ years of managing engineers, with hands-on expertise in Hadoop/Spark, Kafka, and modern data tools.

Qualifications

• 5+ years of hands-on engineering experience in Big Data and distributed systems.
• 2+ years managing engineering teams.
• Expertise with batch data processing frameworks such as Hadoop, Spark, EMR.
• Experience with stream processing systems such as Kafka and RedPanda.
• Proficiency with technologies such as Temporal, Apache Iceberg, AWS Glue, Spark, DBT, and Soda.
• Ownership mindset with ability to translate complex business needs into scalable roadmaps.

Responsibilities

• Lead and manage the Data Platform Engineering team and the end-to-end design of scalable data systems.
• Provide architectural guidance and establish standards for data modeling and processing.
• Build and optimize data abstractions, pipelines, and frameworks for high scalability.
• Translate complex requirements into well-scoped architectural pieces.
• Foster an AI-native engineering culture and integrate AI tools to enhance workflows and automate data ops.
• Manage stakeholder relationships across Engineering and Product and align priorities with OKRs.
• Mentor and coach team members with design reviews and RCA to elevate technical rigor.

Skills

Big Data

Distributed systems

Team management

Data pipelines

Python

Spark

Tools

Hadoop

Spark

EMR

Kafka

RedPanda

Temporal

Apache Iceberg

AWS Glue

dbt

Snowflake

Airflow

Kubernetes

OpenMetadata

Terraform

Job description

What you'll be part of

Garner is on a mission to transform the U.S. healthcare system — and we're the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare "win win" — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can.

About the role

We are seeking an execution-focused and visionary Data Engineering Manager to lead our Data Platform Team. In this critical role, you will manage a team of talented engineers dedicated to building the common, reusable abstractions and high-throughput data infrastructure that power Garner's internal and external applications. You will champion an AI-native development philosophy and scale our data pipeline capabilities, ensuring our systems seamlessly support the heavy computational demands of our next phase of company-wide growth.

Where you will work

This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday.

What you will do

• Lead and manage the Data Platform Engineering team, owning the end-to-end design, implementation, and operational excellence of systems that process complex healthcare data
• Provide high-quality technical contributions and architectural guidance, establishing robust standards for data modeling, processing, and system maintainability
• Build and optimize common data abstractions, pipelines, and frameworks designed for high scalability, ensuring our platform keeps pace with exponential enterprise data growth
• Translate complex, ambiguous business and data requirements into well-scoped, accurately estimated, and composable architectural pieces
• Foster and drive an AI-native engineering culture within the team, integrating cutting-edge AI tools and integrations to enhance development workflows and automate data operations
• Manage and grow stakeholder relationships across Engineering and Product, aligning team priorities with departmental OKRs and broader business goals
• Mentor, coach, and guide your team members, conducting detailed design reviews and managing robust Root Cause Analyses (RCA) to continuously elevate technical rigor

The ideal candidate has

• 5+ years of hands-on engineering experience, with a focus on Big Data and distributed system and infrastructure
• 2+ years of managing engineering teams, with a track record of building and mentoring high performing teams
• Expertise with batch data processing frameworks such as Hadoop, Spark, EMR, etc.
• Experience with stream processing systems such as Kafka and RedPanda
• Proficiency with technologies such as Temporal, Apache iceberg, AWS glue, Spark, DBT, and Soda
• Exhibits strong ownership and a proactive mindset, with a proven ability to translate complex business needs into scalable, actionable technical roadmaps
• Demonstrated ability to operate effectively in dynamic, fast-paced startup environments, with a hands-on approach and a strong commitment to organizational success
• Preference for managing healthcare data standards (e.g. HIPAA, EDI)
• A desire to be a part of our mission to improve the U.S. healthcare system
• A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Technologies we use

• Python, Spark, dbt, SnowFlake, Iceberg, Airflow, OpenMetadata, Kubernetes, NATS, AWS, Terraform, Claude Code

What you'll get here

You'll work on problems that matter, at a company working to change healthcare at scale. You'll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you'll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It's demanding by design. You'll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose.

Compensation Transparency

The target salary range for this position is $310,000 - $385,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more.

Equal Employment Opportunity

Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at [e-mail].