Sr. Manager, Data Engineering

Veracyte•San Diego, CA

20h•$192,825 - $258,000•Hybrid

About The Position

At Veracyte, we offer exciting career opportunities for those interested in joining a pioneering team that is committed to transforming cancer care for patients across the globe. Working at Veracyte enables our employees to not only make a meaningful impact on the lives of patients, but to also learn and grow within a purpose driven environment. This is what we call the Veracyte way – it’s about how we work together, guided by our values, to give clinicians the insights they need to help patients make life-changing decisions. The Senior Manager, Data Engineering will manage an engineering team responsible for building and operating Veracyte’s enterprise data platforms. This role combines people leadership with direct technical ownership and is suited for someone who can move comfortably between architecture, implementation details, production operations, and team development. You will guide the evolution of modern data lake and lakehouse environments, delivering trusted and actionable data to scientific, clinical, operational, and commercial teams. You will partner closely with data science, analytics, product, clinical, software engineering, Security, and IT to translate business and scientific needs into secure, scalable, and maintainable solutions. This position is based in our San Diego office with a hybrid working model or may be US remote.

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 related fields.
5+ years of people-management experience, including responsibility for team objectives, work planning, performance management, development, and staffing decisions.
Demonstrated experience leading a data engineering department or multiple related disciplines and delivering outcomes that affect broader functional or business priorities.
Demonstrated experience designing, building, and operating production data pipelines and data platforms, including data lake or lakehouse architectures.
Strong working knowledge of AWS services such as S3, Glue, Athena, Lambda, IAM, and CloudWatch, or comparable technologies.
Hands-on proficiency with SQL and Python, with the ability to review code, evaluate technical designs, troubleshoot production problems, and guide implementation decisions.
Experience with ETL/ELT frameworks, orchestration tools, distributed data processing, analytical data modeling, and reusable data products or shared platform capabilities.
Practical experience implementing data quality, automated testing, observability, lineage, security, privacy, and regulatory controls.
Experience establishing operational objectives, policies, procedures, work plans, schedules, and resource priorities for a technical team.
Experience managing technical debt and operational risk while continuing to deliver business and product priorities.
Proven ability to collaborate across engineering, scientific, product, clinical, business, Security, and IT teams, including the ability to influence and build alignment in complex situations.
Strong communication skills, including the ability to explain technical decisions, alternatives, risks, and trade-offs clearly to technical and nontechnical audiences.

Nice To Haves

Experience in biotechnology, life sciences, diagnostics, healthcare, or another regulated data environment.
Experience working in environments subject to medical device regulations and quality-management requirements.
Experience working with scientific, clinical, laboratory, LIMS, or bioinformatics data.
Familiarity with ML/AI data workflows and supporting MLOps platforms.
Experience optimizing AWS data-platform cost, reliability, security, and performance.
Experience developing reusable data products, shared platform capabilities, or domain-oriented data architectures.
Knowledge of metadata management, data catalogs, and lineage platforms.
Experience managing budget inputs, vendors, or resource planning for a data engineering or platform team.
Experience helping a team scale its processes without introducing unnecessary bureaucracy.

Responsibilities

Manage and develop a team of data engineers, supporting talent planning, hiring, coaching, performance management, career development, and team effectiveness.
Remain close enough to the technical work to guide architecture, design reviews, code quality, troubleshooting, and key implementation decisions.
Collaborate with business and scientific stakeholders to translate requirements into platform capabilities, delivery plans, and measurable outcomes.
Help establish and maintain data 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, and related technologies such as Snowflake.
Support continuous improvement of engineering practices for CI/CD, automated testing, observability, lineage, documentation, security, privacy, and code quality.
Partner closely with cross-functional teams, including Analytics, Data Science, Product, Clinical, Laboratory, Software Engineering, Security, and IT, to support shared goals and initiatives.
Ensure data-platform security, regulatory compliance, privacy, and quality-management requirements are addressed through appropriate architecture, controls, tooling, documentation, and cross-functional collaboration.
Drive platform delivery and operational excellence, helping ensure solutions meet business needs, performance expectations, and production reliability requirements.
Balance delivery priorities, operational support, platform enhancements, technical debt reduction, vendor relationships, and resource considerations.
Evaluate emerging technologies and recommend adoption based on measurable improvements in maintainability, scalability, reliability, performance, security, and cost.
Communicate technical decisions, alternatives, risks, recommendations, and trade-offs clearly to stakeholders and leaders across multiple functions, and build shared understanding in complex or sensitive situations.
Model collaborative, inclusive leadership by engaging the right stakeholders early, seeking diverse perspectives, committing to decisions, pursuing better solutions, and maintaining high standards for quality and responsible use of company resources.