Sr. Manager, Data Engineering

Role overview The Senior Manager, Data Engineering leads a team responsible for building and operating an enterprise data platform that delivers trusted, actionable data to scientific, clinical, operational, and commercial functions. This hands-on leadership role blends people management with direct technical ownership of modern data lake and lakehouse environments, partnering across analytics, data science, product, clinical, software engineering, security, and IT.

Responsibilities - Lead, coach, and develop a team of data engineers, including hiring, performance management, career development, and team-effectiveness practices. - Stay close to the technical work to guide architecture, design reviews, code quality, troubleshooting, and key implementation decisions. - Translate business and scientific requirements into platform capabilities, delivery plans, and measurable outcomes. - Establish and maintain platform standards, governance practices, architectural guidelines, and operational processes. - Make pragmatic architectural decisions using AWS services such as S3, Glue, Athena, Lambda, IAM, and CloudWatch, along with complementary technologies such as Snowflake. - Drive continuous improvement of CI/CD, automated testing, observability, lineage, documentation, security, privacy, and code-quality practices; balance delivery, operations, enhancements, technical-debt reduction, vendor relationships, and resourcing.

Requirements - Bachelor's degree in computer science, engineering, data science, information systems, or a related field, or equivalent practical experience. - 8+ years of relevant experience in data engineering, software engineering, data platforms, or closely related fields, including 5+ years of people management. - Demonstrated experience leading a data engineering function and delivering outcomes that influence broader business priorities. - Hands-on experience designing, building, and operating production data pipelines and data lake or lakehouse architectures. - Working knowledge of AWS services such as S3, Glue, Athena, Lambda, IAM, and CloudWatch, or comparable cloud platforms. - Proficiency with SQL and Python, plus experience with ETL/ELT frameworks, orchestration tools, distributed data processing, analytical data modeling, and data quality, testing, observability, lineage, and security practices.

Nice to have - Familiarity with regulated environments and embedding security, privacy, and compliance controls into data platforms. - Experience evaluating and adopting emerging technologies based on measurable improvements in maintainability, scalability, reliability, performance, security, and cost.

Benefits and work setup - Hybrid working model based in a San Diego office, or US remote. - Published pay ranges include $199,000–$220,000 (San Diego), $202,000–$258,000 (South San Francisco), and $192,825–$220,000 (US remote), with final compensation dependent on experience, skill set, location, and other factors; the total package may include discretionary bonuses or incentives and restricted stock units.