• Own performance investigations across the product and data stack, moving from slow queries or poor user experiences to measurable root causes and production improvements • Design and evaluate data architecture changes • Partner with Product, frontend, and backend engineers to adapt product behavior to the characteristics of the underlying data through techniques such as request batching, debouncing, caching, pagination… • Develop representative benchmarks for important combinations of filters, datasets, and product workflows • Measure query latency, throughput, concurrency, and infrastructure consumption, with particular attention to datastore performance and cost (we use ClickHouse Cloud) • Evaluate the likely performance impact of upstream dataset changes before they reach production • Assess future product requests and recommend technical or product approaches that balance functionality, responsiveness, scalability, and cost • Prototype competing solutions, clearly communicate their tradeoffs, and validate the selected approach in production • Improve performance instrumentation, monitoring, and regression detection across data-powered product experiences • Establish reusable patterns and capabilities as the Data Product platform becomes a supporting layer for additional products and teams

• Build and maintain reliable data pipelines that process raw claims data from diverse sources to enriched, standardized formats using tools including Python, SQL, PostgreSQL, Trino, ClickHouse, Airflow, Datadog, and AWS cloud services • Build automated observability and monitoring into data quality • Produce privacy-preserving datasets and protect PHI, implementing de-identification and PII-reduction specifications set with Security, Infrastructure, and Product • Draft technical design and documentation • Seek and prioritize technical and product feedback from internal customers • Iterate quickly with an eye towards value

• Design data structures for modern environments (Data Lake, Lakehouse, Data Warehouse); • Support the definition of data architecture and engineering best practices; • Work closely with business areas to understand requirements and design data solutions; • Define and promote standards for data governance, quality, and data organization; • Serve as a technical reference, supporting engineering and analytics teams; • Contribute to the evolution of data pipelines and structures, ensuring adherence to defined models.

• The DataOps Engineer is responsible for designing, building, and operationalizing data infrastructure that powers the organization's analytics and business intelligence capabilities. • This role sits at the intersection of data engineering, cloud architecture, and DevOps - owning end-to-end data pipelines, platform reliability, data quality, and cloud infrastructure in a regulated environment. • Responsibilities span data pipeline development, cloud migrations, platform operations, and infrastructure management across multi-cloud environments. • Lead the end-to-end migration of data workloads from AWS Commercial to AWS GovCloud, including S3, Glue, Redshift, IAM, VPC, Lambda, and CloudWatch. • Identify and resolve service parity gaps between commercial and GovCloud environments; validate data integrity, encryption posture, and access controls post-migration. • Set up and administer enterprise Git repositories (GitHub, Azure DevOps, or GitLab) for the data and analytics team, including branching strategy, access controls, and code review workflows. • Migrate existing scripts, pipelines, and artifacts into version-controlled repositories and enforce DataOps source control best practices across the team. • Ensure all migrated and provisioned resources comply with NIST 800-171, DFARS, CUI, and ITAR/EAR handling requirements. • Administer and optimize cloud data services across AWS GovCloud (S3, Glue, Redshift, Lambda) and Azure Government (Synapse, ADF, ADLS Gen2, Databricks). • Manage infrastructure-as-code for all data platform resources using Terraform and CloudFormation (required); Ansible preferred for configuration management. • Monitor cloud resource utilization, enforce cost governance, and implement right-sizing recommendations for data workloads. • Maintain backup, disaster recovery, and business continuity procedures for all data platform services. • Serve as Tier 3 support for complex cloud infrastructure and data incidents; perform root cause analysis and implement preventive measures.