• Design, develop, and maintain scalable ETL pipelines and data workflows. • Build, optimize, and maintain data processing solutions using Azure Databricks. • Support phased data migration from legacy databases and ETL tools. • Implement layered lakehouse data architectures in Databricks. • Develop data processing notebooks, workflows, and distributed data transformations using Python and PySpark. • Develop data validation, reconciliation, and testing processes. • Integrate Databricks data platforms with analytics and reporting tools. • Support data governance initiatives including metadata management. • Maintain source control and CI/CD pipelines for Databricks workflows. • Provide ongoing support for Databricks workflows and troubleshoot complex issues. • Provide guidance to junior data engineers and contribute to documentation and team enablement.

• Develop and maintain ETL/ELT pipelines to ingest, transform, and load data from various sources including pharmacy claims, eligibility files, and client data feeds • Support automation of file processing workflows, including format standardization and validation • Create and maintain documentation for data models, pipeline architecture, and transformation logic • Implement data validation and quality checks to ensure accuracy and completeness of source data • Troubleshoot and resolve data pipeline failures and anomalies • Support the design and maintenance of dimensional models in our cloud data warehouse • Partner with analytics and reporting teams to understand data requirements and deliver reliable, well-structured datasets • Ensure adherence to compliance and data security standards when handling sensitive information (PHI and PII), including completion of HIPAA training • Participate in code reviews and follow version control best practices

• Responsible for designing, building, and maintaining data pipelines, data integration processes, and data infrastructure. • Collaborate closely with data scientists, analysts, and other stakeholders to ensure efficient data flow and support data-driven decision making across the organization.

• Work collaboratively with data engineers and other partners in the messaging space • Build reliable, scalable data pipelines using Apache Spark/Flink • Power analytic dashboards, custom viz applications, A/B experiments, and algo features • See the problem through a platform lens • Build scalable, flexible data products