• Deliver Flex-critical data needs: build and maintain reliable pipelines and datasets that enable Flex models (e.g., demand/availability signals, aggregations, monitoring). • Evolve the data platform: assess what we have today and drive pragmatic improvements in architecture, tooling, and operating practices. • Own data quality and trust: implement testing, lineage/definitions, and guardrails (e.g., dbt tests, anomaly detection, freshness checks) so stakeholders can trust outputs. • Enable self-serve analytics: produce well modeled datasets and documentation that make it easy for others to answer questions without bespoke work. • Partner on data science work: collaborate on data readiness for modelling, feature pipelines, evaluation workflows, and productionization concerns (even if you’re not the primary model builder). • Make high-leverage tech choices: propose and justify changes (or non-changes) to tools and processes, prioritizing impact and delivery over long platform rewrites.

• Design, build, and maintain scalable data pipelines using Microsoft Fabric and Apache Airflow • Ingest, transform, and integrate data from a variety of sources, including relational systems, APIs, and MongoDB • Implement and manage data solutions aligned to Medallion architecture principles (Bronze, Silver, Gold) • Design and maintain analytical data models, including fact and dimension tables, to support reporting and analytics • Optimize data storage, performance, and reliability across lakehouse and warehouse environments • Ensure data quality, observability, and lineage through validation, monitoring, and documentation • Collaborate with data analysts and BI developers to enable performant, well-modeled datasets for Power BI • Partner with clinical, operational, and technical stakeholders to understand data requirements and constraints • Support data governance, security, and compliance efforts, including HIPAA-related controls • Mentor junior data engineers and contribute to engineering standards and best practices

• This is a remote position • Ability to work effectively in ambiguous environments and handle unstructured problems while anticipating risks and issues • Experience designing and building end-to-end data processing pipelines for Data Lake and Data Warehouse implementations • Hands-on expertise in configuring and developing custom components for data ingestion, processing, and provisioning • Strong experience working on Azure Cloud, including Azure Synapse and SQL • Proven experience delivering at least one complete end-to-end data pipeline implementation • Expertise in architecting enterprise solutions using frameworks, reusable components, and enterprise design patterns • Strong knowledge of database design, development, performance tuning, memory optimization, and DB partitioning • Experience across DW-BI technologies such as MSBI, Oracle, and Teradata • Proficient in CI/CD automation and deployment practices • Programming experience in Python, PySpark, and Scala • Skilled in building dashboards and visualizations using Power BI

• Optimize and modernize Amazon Redshift environments, including consolidation into Redshift Managed Storage (RMS). • Lead or support migration from Provisioned Redshift to Redshift Serverless with zero or minimal downtime. • Analyze and tune Redshift SQL for performance across batch and ad-hoc analytical workloads. • Review and enhance data ingestion pipelines from AWS sources such as RDS, DynamoDB, and Kinesis. • Design data delivery into Apache Iceberg tables and Redshift with minimal duplication and high efficiency. • Collaborate with stakeholders on architecture recommendations and best practices.