Senior Manager, Software Engineering (Data Architecture)

Ambry Genetics • Chicago, IL

8d • $150,000 - $225,000

About The Position

Tempus is seeking a Senior Manager, Software Engineering to lead and mentor a small team of data engineers and data architects and own the enterprise data model and the reliability of the data it represents. The role designs the integration of clinical records, genomics, next-generation sequencing (NGS) laboratory data, and radiology data, ensuring accuracy, traceability, and fitness for applications such as machine learning and clinical decision-making. The manager establishes modeling standards, implements data-pipeline quality gates, develops the team through design and code reviews and ongoing coaching, and uses AI-assisted development tools to accelerate design, testing, and delivery.

The role works with engineers, product managers, scientists, and operators to evolve the enterprise data model across relational and NoSQL data stores. It maintains data architecture documentation, applies healthcare and modern data-modeling standards, evaluates source data for completeness and structural consistency, and designs observability for the data ecosystem. The position also partners with product and engineering leaders on backlog prioritization, estimation, and technical planning.

The position is at the intersection of software engineering and cancer research, shaping the data model and quality systems that support reliable research and clinical use. The stated salary range is $150,000–$225,000; the range may vary by location, and actual salary may depend on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical benefits, and other benefits depending on the position.

Requirements

• Record of leading and mentoring a small team of data engineers and data architects, setting standards, and guiding design in a fast-paced environment.
• Ability to translate data-model and data-quality gaps into clear recommendations for engineers, scientists, and other stakeholders.
• Expertise in the modern data stack, particularly dbt, SQL, and Python, including exceptional SQL skills in an enterprise data warehouse.
• Proficiency with AI coding assistants such as Cursor, Claude, and GitHub Copilot.
• Experience applying modern data architecture best practices and patterns and guiding a team in their use.
• Strong experience with third normal form (3NF) and dimensional (star schema) modeling.
• Experience with healthcare data standards such as FHIR, OMOP, and related standards, and modeling tools such as Erwin, Vertabelo, or Lucidchart.
• Understanding of ETL/ELT and business intelligence architectures, and ability to explain long-term implications of relational, MPP, and NoSQL technology choices.
• Experience architecting solutions across more than one of relational, MPP, and NoSQL technologies.
• Practical experience with data observability, such as Elementary and Grafana, and with profiling and correlation analysis to identify data-integrity problems.
• Healthcare and next-generation sequencing domain experience, including clinical and genomic data.

Nice To Haves

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

Responsibilities

• Lead and mentor a small team of data engineers and data architects through design reviews, code reviews, technical guidance, and ongoing coaching.
• Own and evolve the enterprise data model with engineers, product managers, scientists, and operators, integrating clinical, genomic, NGS laboratory, and radiology data across relational and NoSQL data stores.
• Author and maintain entity-relationship diagrams, data dictionaries, API specifications, and data-translation documentation at conceptual, logical, and physical levels.
• Apply modern data architecture practices and teach modeling standards, including third normal form, dimensional (star schema) design, and healthcare standards such as FHIR and OMOP.
• Profile source-system models and partner data to assess completeness and resolve structural inconsistencies before data reaches downstream consumers.
• Design data observability using BigQuery, dbt, Elementary, and Grafana, including monitors, alerts, dashboards, lineage, and source-system traceability.
• Partner with product managers and engineering leads to prioritize the backlog, estimate work, and translate requirements into actionable technical tasks.
• Advance AI-assisted development using tools such as Cursor, Claude, and GitHub Copilot for modeling, testing, refactoring, and pipeline development.
• Establish modeling standards and implement quality gates in data pipelines.

Benefits

• Full range of benefits, which may include incentive compensation, restricted stock units, medical benefits, and other benefits depending on the position.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed