Sr. Manager, Data Engineering

veracyte; United States; Hybrid; USD 199,000 - 258,000; Full time

Benefits offered by this job

Hybrid work model

Discretionary bonus

Restricted stock units

Job summary

Veracyte seeks a Senior Manager, Data Engineering to lead a team building an enterprise data platform delivering trusted data across scientific, clinical, operational, and commercial functions. You will own modern data lake/lakehouse environments and partner with analytics, data science, product, clinical, software engineering, security, and IT.

The role combines people leadership with hands-on technical work, including guiding AWS services (S3, Glue, Athena, Lambda, IAM, CloudWatch) and

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8+ years in data/software engineering, with 5+ years in people management.
• Experience leading a data engineering function delivering business outcomes.
• Hands-on experience designing and operating production data pipelines and lakehouse architectures.
• Proficiency with SQL and Python; experience with ETL/ELT and data quality, observability.

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.

Skills

Leadership

Team management

Data architecture

Data pipelines

Communication

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

Snowflake

AWS

CI/CD

Job description

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.