• Design, implement, and optimize data pipelines using both batch and streaming processing frameworks. • Architect and maintain data lakehouse solutions using Apache Iceberg and object storage such as S3. • Implement scalable Data Vault and Star Schema models. • Build and manage real-time ingestion pipelines with Kafka, Spark, or Flink. • Integrate and orchestrate workflows using tools like Airflow, dbt, NiFi, or Airbyte. • Enforce data governance, data quality, and access control policies. • Troubleshoot pipeline performance and reliability issues.

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows • Help to own and improve our data warehouse architecture in Snowflake • Implement data modeling best practices to support analytics and reporting needs • Drive data quality initiatives, including validation, testing, and monitoring frameworks • Partner with product engineering teams to design data contracts and integrate data capture into applications • Work closely with Analytics and Business Intelligence teams to understand reporting needs and deliver reliable data products • Collaborate with Security and Compliance to implement data governance and privacy best practices • Partner with Product and Engineering leadership on data strategy and technical roadmaps • Collaborate with Clinicians and Clinical Researchers to ensure they have the data available to deliver on their projects and support patient care • Work with Analysts to decrease latency, improve testability and integrate with de-identification providers to meet our goals and regulatory needs • Mentor and coach more junior data engineers, fostering growth and technical development • Establish and document data engineering standards, patterns, and best practices • Champion a culture of data quality, automation, and continuous improvement

• Design, develop, and maintain scalable ETL (Extract, Transform, Load) pipelines to process large volumes of data from diverse sources. • Build and optimize data storage solutions, such as data lakes and data warehouses, to ensure efficient data retrieval and processing. • Integrate structured and unstructured data from various internal and external systems to create a unified view for analysis. • Ensure data accuracy, consistency, and completeness through rigorous validation, cleansing, and transformation processes. • Maintain comprehensive documentation for data processes, tools, and systems while promoting best practices for efficient workflows. • Collaborate with product managers, and other stakeholders to gather requirements and translate them into technical solutions. • Participate in requirement analysis sessions to understand business needs and user requirements. • Provide technical insights and recommendations during the requirements-gathering process. • Participate in Agile development processes, including sprint planning, daily stand-ups, and sprint reviews. • Work closely with Agile teams to deliver software solutions on time and within scope. • Adapt to changing priorities and requirements in a fast-paced Agile environment. • Conduct thorough testing and debugging to ensure the reliability, security, and performance of applications. • Write unit tests and validate the functionality of developed features and individual elements. • Writing integration tests to ensure different elements within a given application function as intended and meet desired requirements. • Identify and resolve software defects, code smells, and performance bottlenecks. • Stay updated with the latest technologies and trends in full-stack development. • Propose innovative solutions to improve the performance, security, scalability, and maintainability of applications. • Continuously seek opportunities to optimize and refactor existing codebase for better efficiency. • Stay up-to-date with cloud platforms such as AWS, Azure, or Google Cloud Platform. • Collaborate effectively with cross-functional teams, including testers, and product managers. • Foster a collaborative and inclusive work environment where ideas are shared and valued.

• Build, maintain, and optimize data pipelines using Spark, Kafka, Airflow, and Python • Orchestrate workflows across GCP (GCS, BigQuery, Composer) and AWS-based systems • Model data using dbt, with an emphasis on quality, reuse, and documentation • Ingest, clean, and normalize data from third-party sources such as Google Ads, Meta, Taboola, Outbrain, and Google Analytics • Write high-performance SQL and support analytics and reporting teams in self-serve data access • Monitor and improve data quality, lineage, and governance across critical workflows • Collaborate with engineers, analysts, and business partners across the US, UK, and India