In this senior role, you will lead the design, development, testing, and deployment of highly scalable, high-performance data integration and transformation solutions across Quest’s enterprise data platform and the HAS data product. Partnering with architects, business customers, and cross-functional engineering and data science teams, you will shape data architecture strategy, design large-scale data processing solutions, and build the data-driven systems that guide Quest’s reporting and analytics. This includes defining standards, reusable patterns, and best practices for mining, acquiring, transforming, standardizing, enhancing, migrating, verifying, and modeling Quest’s enterprise data. In addition, you will drive advanced analytics initiatives that build, train, deploy, and refine AI/ML models to efficiently analyze vast quantities of data, and you will provide technical leadership and mentorship to fellow engineers to best serve our patient population.

• Serve as a senior technical advisor and subject matter expert to business customers, architects, and internal teams, solving the most complex data challenges related to healthcare analytics.
• Lead the design and architecture of end-to-end data solutions, translating business requirements into scalable, reusable, and well-documented technical designs.
• Define, drive, and govern data architecture standards, design patterns, and engineering best practices across the data engineering organization.
• Engineer and oversee the preparation of internal and customer-facing datasets, ensuring strict adherence to defined technical specifications, internal data standards, and external Statements of Work (SOWs).
• Architect, develop, and optimize robust, scalable data pipelines to acquire, transform, and provision data for analytics and data science initiatives.
• Design and build performant, scalable data models and warehouse structures within cloud data warehouses (e.g., Google BigQuery, Snowflake) and guide their long-term evolution.
• Partner with SD3 Data Scientists to productionize, operationalize, and manage the handoff of machine learning model inferences into our persistent data stores.
• Establish and champion modern DevSecOps standards, including CI/CD, automated testing, and version control using GitHub, across the team.
• Ensure all data solutions comply with data governance, security policies, and healthcare regulations (e.g., HIPAA), and help define and improve those policies.
• Provide technical leadership and mentorship to junior and mid-level engineers, including leading design reviews and code reviews.
• Lead organizational improvements in processes and technology by evaluating, recommending, and adopting new tools and best practices in data engineering.
• Define and lead unit, integration, and performance testing strategies to ensure the quality, reliability, and scalability of data pipelines.

Required Work Experience: 

• 5-8 years of data development experience with a focus on designing and building data pipelines and ETL processes
• 5-8 years of experience with the cloud (AWS, Azure and/or Google Cloud Platform) – GCP experience highly preferred
• 5-8 years of experience in cloud-based data warehouses (Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse Analytics)
• 5-8 years of experience with cloud-based ETL/ELT tools (Matillion, Glue, Data Factory). Matillion experience is strongly preferred.
• 2+ years in a technical lead or data/solution architecture capacity, leading the design of large-scale data solutions
• Bachelor’s Degree (Computer Science, Engineering, Information Systems, Mathematics, Business Analytics, or relevant degree)

Preferred Work Experience: 

• Experience with version control systems (Git) and leading DevSecOps / CI-CD practices
• Understanding of and willingness to embrace Agile Principles (Scrum), including serving as a technical lead
• Experience mentoring engineers and conducting design and code reviews
• Experience defining data architecture standards and data governance across teams
• Physical and Mental Requirements: 
• Open mindset, ability to quickly adapt new technologies and learn new practice
• Master’s Degree (Computer Science, Engineering, Information Systems, Mathematics, Business Analytics, or relevant degree)

Knowledge: 

• Demonstrated advanced knowledge of SQL, Java, Python, C/C++, Scala, Julia, and/or other modern data and analytics programming languages
• Demonstrated expertise in data modeling principles, data architecture, and database systems.
• Familiarity with containerization technologies (e.g., Docker, Kubernetes) is a plus.
• Knowledge of data architecture frameworks, distributed systems, and performance optimization at scale

Skills:

• Data solution design and architecture
• Coding
• Data Warehousing
• Database schema optimization
• Database Systems
• Technical leadership and mentoring
• Critical thinking skills
• Business communication
• ETL