Data Engineering Team Lead

Position Summary:

MacroHealth has a rare opportunity to play a critical role in optimizing healthcare delivery and payments using applied intelligence at intersection healthcare and technology.  MacroHealth’s Data Foundation team is growing and looking for a Data Engineering Team Lead to join.

The Data Foundation team uses Azure Databricks, and modern distributed/big data technologies (e.g. Hadoop, Hive, Kafka) to create data-driven value-add SaaS and Analytics solutions. The Data Foundation aggregates data across multiple disparate sources; normalizes the data; builds and apply intelligence models including AI/ML to create both large, enterprise, scalable applications, and open API-based ecosystems.  

The candidate will be spending 70% of their time designing and implementing high quality, innovative and state of the art Data Infrastructure that complies with various compliance and follows industry best practice; and 30% time leading and mentoring other team members in designing and developing features. The candidate will contribute to teamwork and the development of a positive work culture.  

This candidate would be responsible for leadership and coaching activities in the team including actively participating in code and design reviews, mentoring, and helping craft and deliver performance reviews. This role is a great fit for someone who is excited to grow into engineering leadership over time. It offers the chance to take on increasing scope and develop toward a future manager track as you demonstrate the ability to lead and as the business' needs evolve and scale. 

Key Relationships: Direct reports (Data Engineers), peer engineering teams, senior engineering team members, architecture team, product managers, and cross-functional stakeholders. 

Key Accountabilities:   

1. Technical Leadership and Best Practices 

• Lead technical discussions, code reviews, and architecture planning sessions.
• Evaluate, select, and advocate for the right tools and technologies to support the data platform and ensure scalability and maintainability.
• Own architectural decisions for the core data foundation, including data modeling, storage layers, orchestration frameworks, and metadata management tools.
• Support data privacy and compliance efforts (e.g., HIPAA, SOC 2) by embedding governance into data workflows.
• Set standards for data quality, testing, version control, and deployment across all stages of the data lifecycle.
• Drive a culture of operational excellence through documentation, automation, and proactive monitoring. 

2. Team Leadership and People Management 

• Lead, coach, and support a team of Data Engineers; providing regular feedback, and career development to support continuous learning.
• Establish clear ownership and ensure accountability for team deliverables and commitments
• Facilitate team meetings, drive sprint planning, and support agile delivery.
• Promote a culture of collaboration, continuous improvement, and psychological safety. 

3. Strategic and Operational Planning

• Collaborate with engineering and product leadership to align Data Foundation team’s priorities with business and technical goals.
• Own team roadmap and project delivery timelines; translating business priorities into actionable technical plans and team backlogs.
• Continuously assess and optimize bottlenecks and opportunities to improve operational efficiency. 

4. Hands-on Engineering  

• Actively contribute to the design, development, and maintenance of scalable, production-grade data pipelines using modern technologies (e.g., Databricks, Airflow, Spark, Kafka).
• Lead the implementation of data platform components, including ingestion frameworks, transformation layers, orchestration systems, and observability tooling.
• Ensure high availability, reliability, and performance of data systems through monitoring, alerting, and operational best practices.
• Automate workflows to reduce manual overhead and increase developer productivity (e.g., data validation frameworks, schema enforcement, test automation).
• Continuously evaluate and improve the technical stack and infrastructure to support evolving data and scalability needs. 

5. Cross-Functional Collaboration 

• Partner closely with product managers, data analysts, data engineers, and business stakeholders to understand data needs, pain points, and future use cases.
• Work with software engineers and platform application teams to define data contract boundaries, implement event-based data models, and establish reliable data sources.
• Serve as the technical liaison between data infrastructure and downstream data consumers, ensuring alignment on definitions, metrics, and SLAs.
• Help promote a data-as-a-product mindset by advocating for clear ownership, consistent standards, and high-quality data delivery across domains. 

Knowledge, Skills and Abilities: 

• Deep understanding of data architecture, pipelines, and distributed systems.
• Proficient in Python, and data engineering frameworks (e.g., Databricks, Spark, Airflow).
• Strong command of data modeling concepts (e.g., star/snowflake schemas, normalized/denormalized structures, dimensional modeling).
• Experience implementing and operationalizing data governance policies (e.g., data lineage, access control, data contracts).
• Familiarity with data cataloging and metadata management tools.
• Ability to establish and maintain standards for data quality, discoverability, and consistency.
• Proven ability to lead and mentor engineers; support their technical growth and performance.
• Experience operating in regulated environments with security and compliance needs.
• Proactive problem solver with a continuous improvement mindset. 

Required Education and Experience: 

• Bachelor’s degree in Computer Science or related field, or equivalent work experience.
• 7+ years of professional experience as a software developer or data engineer, ideally within the healthcare industry.
• 1+ years of formal leadership experience or serving in a Team Lead capacity to include leading code and design reviews, mentoring engineers, and helping craft and deliver performance reviews.
• Experience with modern big data technologies such as Databricks, Hadoop, Hive, Kafka etc. 
• A solid foundation in object-oriented languages.
• Experience designing and building solutions within a cloud-based microservice architecture. 
• Experience in working with Product Management and other stakeholders to help define product direction and requirements. Demonstrated competence as a technical owner of large SaaS/IaaS systems spanning multiple components.  

Preferred Experience: 

• Experience with healthcare data (e.g. health payments, authorizations, eligibility, electronic health records) 
• Experience with existing and emerging health care interoperability technologies and standards (e.g. X12, NCPDP, FHIR) 
• Experience working for or with healthcare providers/plans/payers particularly in data warehousing and business intelligence.  

Location: Remote anywhere within the US (Must be located in the US)

Core Competencies: 

One Team:  

Act as one team with fellow MacroMates and customers  

Value humility, low ego, and collaboration  

Maintain an All for One, One for All attitude   

Deliver on Promises:  

Do the right thing  

Do what you say you will do  

Work with a sense of urgency and transparency 

Macro Thinking:   

Challenge yourself and others to think boldly, bigger, and into the future  

Lead with a Growth Mindset  

Act as a thought leader for the healthcare industry 

MacroHealth is an equal opportunity employer.