Director, Data Engineering

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. 

Our Values:

• We Seek A Better Way: We pursue bold ideas, embrace complexity, and keep pushing forward.


• We Make It Happen: We act with urgency, deliver with excellence, and always find a way. 


• We Are Stronger Together: We engage with empathy, align around what's best for Veracyte, and celebrate as one team. 


• We Care Deeply: We show up with integrity, kindness, and respect for one another. 

The Position:

The Director of Data Engineering will lead the design, development, and evolution of our enterprise data platforms to empower scientific, clinical, operational, and commercial teams with trusted, actionable data. This role will oversee the architecture and engineering of modern data lake and lake house environments on AWS, ensuring scalable, secure, reliable, and efficient data solutions that support Veracyte’s mission. You will partner cross-functionally to shape the future of our data ecosystem while developing and mentoring a high-performing engineering team. This position is based out of our San Diego office (hybrid).

Responsibilities 

• Lead the strategy, roadmap, and execution for data engineering across cloud-native data lake and lake house architectures on AWS


• Oversee ingestion, transformation, quality, governance, and orchestration pipelines supporting analytics, ML, bioinformatics, and operational workloads


•  Drive architectural decisions for scalable, high‑availability systems using AWS services such as S3, Glue, Athena, Lambda, and related technologies like Snowflake.


• Establish and enforce data engineering best practices, including CI/CD, testing frameworks, observability, lineage, and documentation 


• Partner with data science, analytics, product, clinical, and software engineering teams to deliver reliable, well-modeled data products


• Ensure platform security, compliance, and data privacy through policies, tooling, and close collaboration with Security and IT


• Manage, grow, and mentor a team of data engineers and architects, fostering an inclusive, collaborative culture aligned with our values


• Evaluate emerging technologies and guide modernization initiatives to improve scalability, cost efficiency, and performance


• Collaborate with stakeholders to translate business and scientific needs into scalable and maintainable data solutions


• Own operational excellence for all production data pipelines, including monitoring, incident response, and SLA management

Who You Are:

Required Qualifications 

• 10+ years of experience in data engineering or related fields, including 5+ years building large-scale data architectures


• Demonstrated expertise designing and operating data lake and lake house architectures on AWS 


• Hands-on experience with modern ETL/ELT frameworks, distributed data processing, and orchestration tools


•  Strong proficiency with SQL, Python, and data modeling for analytical and operational workloads 


• Experience leading and mentoring engineering teams, including hiring, performance management, and career development


• Deep understanding of data governance, data quality, security, and privacy controls


• Proven ability to collaborate effectively with cross-functional partners in a fast-paced environment


• Strong communication skills with the ability to translate complex technical concepts into understandable terms 

Preferred Qualifications 

• Experience in biotech, life sciences, diagnostics, or other regulated data environments


• Background working with scientific, clinical, or laboratory data pipelines


•  Familiarity with ML/AI workflows and supporting MLOps platforms 


• Experience with cost optimization strategies for large-scale AWS data systems


• Expertise in designing reusable data products or domain-oriented architectures 


• Knowledge of metadata management, lineage tooling, and data catalog platforms

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