Manager, Data Engineering

Position Description

The Manager, Data Engineering is a hands-on leader who leads a team of data engineers in executing the full spectrum of data movement - end-to-end data pipelines for ingesting, transforming, and delivering structured and unstructured data. This role collaborates with cross-functional teams to ensure data is reliable, accessible, and optimized for performance.

The ideal candidate brings both the hands-on technical depth to stabilize and improve existing data pipelines and the strategic mindset to help build what comes next. By setting priorities, managing capacity, and upholding high standards for data quality, governance, and documentation, the role ensures reliable delivery of mission-critical datasets and data products while driving automation and continuous improvement across UNOS's data landscape.

Key Responsibilities

People Leadership & Performance Management

• Lead, mentor, and develop a high-performing team of data engineers and analytics engineers, including onboarding, performance management, coaching, and succession planning.
• Own prioritization and delivery planning by translating analytics and research needs into an executable roadmap aligned to departmental objectives and stakeholder commitments.
• Serve as the escalation point for complex technical and cross-functional issues, removing blockers and ensuring delivery remains on track.
• Maintain and grow team-wide domain expertise to ensure solutions reflect clinical and operational context.

Data Management & Architecture

• Provide hands-on technical leadership in data modeling, data lake design, optimization, CI/CD practices, and troubleshooting.
• Oversee the design, build, and maintenance of enterprise-grade data solutions, including secure pipelines, architectures, schemas, and curated datasets/data marts.
• Design, build, and maintain scalable ETL/ELT pipelines with a focus on reliability, performance, and long-term maintainability.
• Ensure operational stability and continuity of mission-critical OPTN analytical datasets through effective lifecycle management and reliability practices.
• Enable complex data integration and linkage across multiple internal and external data sources.
• Advance platform modernization and continuous improvement aligned to enterprise direction and long-term sustainability.
• Lead the assessment and modernization of legacy SAS-based analytical pipelines, developing a phased transition plan to replace or augment them with modern ELT tooling and cloud-native equivalents.

Data Quality, Governance & Engineering Standards

• Establish and enforce engineering and programming standards, including documentation, naming conventions, reusable components, peer review, and troubleshooting playbooks.
• Implement rigorous data quality, validation, monitoring, and audit controls to ensure accuracy, reproducibility, and compliance with HIPAA, patient-data privacy, and applicable regulatory requirements.
• Ensure data pipelines and analytical datasets adhere to privacy-by-design principles, including appropriate access and security controls, data minimization, and secure handling of protected health information (PHI).
• Analyze production issues, identify root causes, and improve processes to prevent recurrence while safeguarding data integrity and compliance.
• Drive automation and scalable self-service patterns to reduce manual effort, improve reliability, and accelerate compliant data delivery.
• Ensure data engineering outputs meet the reliability, latency, and quality standards required to support customer-facing data analytics products.

AI Enablement & ML Workflow Implementation

• Build and maintain AI-ready data foundations, ensuring datasets are well-structured, governed, and suitable for machine learning and advanced analytics use cases.
• Collaborate with Data Science, Software Engineering, and Analytics teams to support ML workflows, including feature data generation, training/validation datasets, and reproducible pipelines.
• Enable scalable and secure ML/AI data pipelines that support experimentation, model iteration, and downstream operationalization.
• Establish standards and processes that support responsible AI, including data lineage, monitoring, auditability, and alignment with privacy and compliance requirements.

Cross-Functional Collaboration & Strategy

• Partner with Product, Research, Information Security, Technology and Engineering organizations to deliver scalable, reliable data solutions.
• Contribute to team, product, and platform strategy discussions to ensure data platforms align with organizational goals, AI readiness, and future needs.
• Support the development of new data ingestion pathways from external health data sources, enabling UNOS to build data assets.
• Navigate a complex stakeholder environment, building productive relationships across the organization to align data engineering priorities with organizational needs.

Minimum Requirements

• 5+ years of hands-on experience in data engineering, platform engineering or data architecture
• 2+ years' experience leading or mentoring engineers and data architects

Critical Skills

• Demonstrated experience managing or leading data pipeline modernization or cloud migration initiatives.
• Experience building or operating data platforms that support both operational and analytical workloads simultaneously.
• Demonstrated experience delivering enterprise-scale data solutions in on-prem and Azure cloud environments.
• Hands-on expertise with modern cloud data platforms and tools, including Azure, Databricks, Spark, Python (pandas, PySpark), Azure Synapse, and Azure Data Lake technologies.
• Strong understanding of data modeling, ETL/ELT pipeline design, orchestration, and production data operations.
• Deep proficiency with MS SQL Server, SSIS, Azure Data Factory, Azure Functions, and modern data warehousing concepts.
• Knowledge of CI/CD, DevOps practices, version control, and agile development methodologies.

Additional Skills & Qualifications

• Familiarity with data governance, privacy requirements, and compliance frameworks.
• Strong leadership, communication, and collaboration skills, with the ability to translate complex technical concepts for non-technical audiences.
• Proven ability to manage competing priorities, adjust plans to evolving needs, and drive measurable outcomes.
• Advanced problem-solving skills with the ability to resolve moderately complex data, pipeline, or integration issues.
• Ability to influence and guide stakeholders across technical and non-technical teams.
• Experience with clinical, scientific, or research-oriented datasets is a plus, along with the ability to support scientific requirements gathering and documentation.

Education

• Bachelor's degree in data engineering, Data Science, Computer Science, Analytics, Informatics, or a related discipline with strong demonstrated, hands-on experience in data engineering
• Master's degree is preferred

Physical Requirements

• General office demands
• Prolonged periods of sitting at a desk and working on a computer.
• Frequent reaching, handling, and fine manipulation for using office equipment, filing, and managing paperwork.
• Manual dexterity sufficient to operate a keyboard, mouse, and other office tools.
• Occasional standing, walking, and bending.
• Ability to lift up to 10-20 pounds occasionally.
• Vision abilities required include close vision for computer work and reading documents.
• Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.