Senior Manager, Software Engineering (Data Architecture)

The Role

As Senior Manager, Software Engineering, you will lead and mentor a small team of data engineers and data architects, and you will own the enterprise data model and the reliability of the data it represents. You will design how clinical records, genomics, next-generation sequencing (NGS) laboratory data, and radiology data are integrated, and you will ensure that the data are accurate, traceable, and suitable for critical applications such as machine learning and clinical decision-making.

You will establish modeling standards, implement quality gates in data pipelines, and develop the team through design review, code review, and ongoing coaching. You will use development tools assisted by artificial intelligence (AI) to accelerate design, testing, and delivery.

What You Will Do

• Lead and mentor a small team of data engineers and data architects by conducting design and code reviews, providing technical guidance, and fostering a culture of excellence in data architecture and data quality.
• Own and evolve an enterprise data model with engineers, product managers, scientists, and operators, integrating structured data from clinical records, genomics, the NGS laboratory, and radiology across relational and NoSQL data stores. Author and maintain entity-relationship diagrams, data dictionaries, API specifications, and data-translation documentation at the conceptual, logical, and physical levels.
• Apply modern data architecture best practices and patterns, and teach modeling standards, including third normal form, dimensional (star schema) design, and established healthcare data model standards such as FHIR, OMOP, and related standards.
• Evaluate the completeness of source-system models and partner data through profiling, and resolve structural inconsistencies before they reach downstream consumers.
• Design observability for the data ecosystem with BigQuery, dbt, Elementary, and Grafana, including monitors, alerts, dashboards, lineage, and traceability back to source systems.
• Partner with product managers and engineering leads to prioritize the backlog, estimate work, and translate complex requirements into actionable technical tasks. Advance the use of AI-assisted development, including Cursor, Claude, and GitHub Copilot, to accelerate modeling, testing, refactoring, and pipeline development.

Why We Are Looking for You

• You have a record of leading and mentoring a small team of data engineers and data architects, of setting standards, and of guiding design in a fast-paced environment. You can translate gaps in the data model and in data quality into clear recommendations for engineers, scientists, and other stakeholders.
• You are an expert in the modern data stack, particularly dbt, SQL, and Python, and you have demonstrated exceptional SQL skills in an enterprise data warehouse. You are proficient in the use of AI coding assistants such as Cursor, Claude, and GitHub Copilot.
• You have applied modern data architecture best practices and patterns, and you can guide a team in their use. You have strong experience with third normal form (3NF) and dimensional (star schema) modeling; with established healthcare data model standards such as FHIR, OMOP, and related standards; and with modeling tools such as Erwin, Vertabelo, or Lucidchart.
• You understand ETL/ELT and business intelligence (BI) architectures, and you can explain the long-term implications of choosing among relational, massively parallel processing (MPP), and NoSQL technologies. You have architected solutions across more than one of these technologies.
• You have practical experience with data observability (for example, Elementary and Grafana) and with profiling and correlation analysis to identify data-integrity problems.
• You have domain experience in healthcare and next-generation sequencing, including clinical and genomic data.

Preferred

• Deep knowledge of Google Cloud Platform (GCP) and BigQuery architecture.
• Experience modeling NGS data output.
• Experience in implementing master data, reference data, or metadata management.