Data Engineering Manager – Healthcare Data Platforms
Location: 100% Remote
Employment: Full-time, Exempt
Compensation: 200,000-220,000
Work Authorization: US Citizens, Green Card Holders, GC EAD, And H4 only
About the Company
A growing healthcare technology organization is developing sophisticated SaaS analytics and workflow solutions that help public-sector healthcare organizations make more informed decisions, improve operational oversight, and deliver better outcomes.
The company works with complex healthcare datasets and is investing in scalable, reusable data engineering capabilities to support customer onboarding, product development, and future innovation.
This is an opportunity to join an established technology organization where data engineering plays a central role in product delivery and customer success.
About the Role
We're looking for a Data Engineering Manager who combines deep technical expertise with proven people leadership and the ability to run an effective engineering function.
This is a true player/coach position.
You'll lead an experienced Data Engineering team while remaining hands-on with complex engineering challenges, architecture decisions, and high-impact initiatives.
The team operates across two closely connected areas: onboarding complex customer data into SaaS products and building reusable data processing capabilities that can scale across the platform.
You'll own the technical direction, people development, priorities, resources, and day-to-day effectiveness of the Data Engineering function.
The ideal candidate has progressed from a strong hands-on engineering background into leadership but has maintained the technical depth required to challenge architectural decisions, solve difficult problems, and personally contribute when necessary.
Key Responsibilities
Technical Leadership
- Provide technical direction across data architecture, solution design, data modeling, ELT, automation, and data quality.
- Remain hands-on with complex engineering initiatives and help resolve difficult technical challenges.
- Lead the development of scalable, reusable data processing capabilities.
- Drive modernization of existing data architectures and engineering practices.
- Evaluate emerging technologies, including AI-assisted tools that improve data processing, quality, and engineering productivity.
- Establish engineering standards, documentation practices, and technical design processes.
- Guide architectural decisions while clearly communicating assumptions, alternatives, tradeoffs, and recommendations.
- Lead the design and optimization of data pipelines, transformations, and standardized data models.
Engineering Operations & Delivery
- Own the day-to-day execution and effectiveness of the Data Engineering function.
- Translate customer, product, and engineering priorities into actionable plans and resource allocation.
- Balance customer data onboarding, platform development, modernization, and technical debt.
- Manage multiple concurrent engineering initiatives, including dependencies, delivery commitments, and competing priorities.
- Establish visibility into engineering capacity, timelines, risks, and delivery status.
- Partner with Product, Engineering, Customer Success, and leadership to align technical priorities with business needs.
- Develop and maintain a technical roadmap that addresses recurring customer and product requirements.
People Leadership
- Lead, coach, and develop an experienced team of Data Engineers.
- Establish clear performance expectations and provide ongoing feedback and career development.
- Support hiring, onboarding, mentoring, and knowledge sharing.
- Identify technical skill gaps and resource constraints.
- Build a culture of accountability, collaboration, technical rigor, and continuous improvement.
- Raise the team's overall engineering capability through strong technical leadership and effective management practices.
Must Haves
- 12+ years of relevant engineering experience.
- 5+ years of direct engineering management experience.
- Proven experience managing a Data Engineering function, including people, priorities, resources, processes, and delivery.
- Deep hands-on Data Engineering experience with SQL and Python.
- Strong experience designing and building scalable ELT pipelines, transformations, and data models.
- Experience across relational databases, MPP platforms, and file-based data architectures.
- Hands-on experience with modern orchestration and transformation frameworks such as Airflow, Astronomer, Dagster, dbt, or Windmill.
- Experience operating, monitoring, and optimizing production data pipelines.
- Experience integrating complex datasets from multiple sources into standardized data models.
- Strong data profiling, validation, quality monitoring, and automation experience.
- Experience designing automated data quality capabilities that measure completeness, consistency, density, and data gaps.
- Proven ability to lead data platform modernization and architectural transformation.
- Experience migrating legacy relational database-centric environments toward distributed, file-based, or cloud-native architectures.
- Experience applying AI-assisted technologies to improve Data Engineering workflows, automation, data quality, or productivity.
- Strong architectural judgment and the ability to evaluate technical alternatives.
- Experience establishing engineering standards and guiding complex technical decisions.
- Experience developing technical roadmaps and balancing competing engineering priorities.
- Experience working across multiple engineering environments, organizations, or stages of growth.
- Healthcare data experience.
- Strong communication skills and the ability to explain technical tradeoffs to both engineering teams and business leadership.
- Bachelor's degree in Computer Science or a related field, or equivalent experience.
What Makes Someone Successful Here
The strongest candidates will combine three capabilities that are not always found together: deep hands-on Data Engineering expertise, experienced people leadership, and the ability to modernize a data platform while maintaining reliable day-to-day delivery.
You should be equally comfortable reviewing architecture, helping an engineer solve a difficult pipeline problem, managing competing delivery priorities, and coaching experienced technical professionals.
You'll understand how to move beyond one-off customer integrations toward reusable engineering capabilities that improve scalability, consistency, and operational efficiency.
You'll also bring the judgment to determine when existing technologies and processes should be improved, when they should be replaced, and how to introduce modernization without disrupting customer commitments.
Healthcare data experience is important because the role requires an understanding of complex source systems, data quality challenges, and the relationship between technical solutions and real-world healthcare use cases.
Why Join?
Lead with technical credibility. Remain close to architecture and engineering while leading an experienced Data Engineering team.
Own the engineering function. Influence people, priorities, delivery practices, technical direction, and the team's long-term development.
Drive meaningful modernization. Help evolve legacy data architectures into scalable, reusable, cloud-native data processing capabilities.
Work with complex healthcare data. Solve challenging integration, modeling, and data quality problems that directly affect customer-facing products.
Shape the future of Data Engineering. Explore AI-assisted engineering capabilities, automation, and emerging technologies that can improve the platform and team productivity.
100% Remote. Lead a distributed engineering function while working closely with Product, Engineering, Customer Success, and company leadership.
The organization offers a comprehensive benefits package that includes medical, dental, and vision insurance, company-paid life and disability insurance, a 401(k) with company match, a vacation travel stipend, and generous paid time off.
If you're an experienced Data Engineering leader who enjoys developing engineers, solving complex technical problems, and building scalable data platforms, we'd like to hear from you.