• Design and own Ceresti’s end-to-end data architecture: a landing zone with secure cloud object storage for raw partner files and API payloads, validated ingestion pipelines into our transactional Postgres, and a curated analytics layer that decouples reporting and AI workloads from production • Build ingestion pipelines for the data we receive today, including partner data files (CSV/JSON/XML/HL7/X12 as applicable) and REST/SFTP API integrations with schema validation, quarantine of bad records, and full lineage from raw bytes to curated row • Stand up and operate the curated layer (data warehouse / lakehouse-lite) so analytics and ML models can consume data without slowing down the transactional system • Choose, integrate, and operate the smallest set of tools needed, including object storage, an orchestrator (Dagster, Prefect, Airflow, etc.), dbt or similar for transformations, a single validation library (Great Expectations / Pandera / Soda) • Design and enforce data governance for a HIPAA-regulated environment: PHI/PII classification, encryption in transit and at rest, role-based access, audit logging, retention and minimum-necessary policies, and de-identification where appropriate • Partner with backend, ML, product, and clinical stakeholders to define data contracts with our health plan and ACO partners and hold the line on data quality • Build and maintain reliable feature data for ML models, including embeddings (e.g., pgvector) and curated feature tables for risk stratification, engagement, and outcomes work • Instrument the data platform for observability including pipeline SLAs, data freshness, schema drift, quality metrics, and act on what the data tells you • Participate fully in our Agile process: backlog grooming, sprint planning, demos, and retrospectives • Mentor engineers across the team on SQL, schema design, and the craft of building data systems that are boring in the best possible way

• Lead a team of data engineers, ensuring alignment on goals, quality and delivery timelines. • Mentor and coach team members to support their technical and professional growth. • Drive engineering excellence by promoting best practices in coding, architecture, testing and observability. • Plan and manage team capacity, sprints and milestones to ensure predictable delivery. • Own the design, evolution and operation of ingestion and transformation pipelines on Apache Airflow and the analytical serving layer on Apache Druid. • Make architectural calls on concurrency, partitioning, memory sizing and cost — including JVM heap and direct-memory tuning on the Druid cluster. • Collaborate closely with DevOps on the Kubernetes / EKS platform that hosts our Druid and Airflow workloads. • Ensure robust data validation, reconciliation and verification so that reporting is trustworthy. • Collaborate with other Team Leaders, Development Managers, Architects and Product Owners to align engineering execution with business objectives. • Contribute to the evolution of development processes, CI/CD pipelines and DevOps practices. • Foster a culture of continuous improvement, innovation and knowledge sharing.

• Design, develop, and maintain scalable RAG/CAG pipelines for AI-powered applications • Build and optimize document ingestion workflows for structured and unstructured data sources • Manage and maintain vector stores to support semantic search and retrieval capabilities • Develop OCR processing pipelines for historical and modern document collections spanning 1781–2025 • Optimize retrieval performance, relevance tuning, and ranking strategies for LLM-based systems • Build reliable data pipelines that support integrations with large language models and AI services • Collaborate with engineers, UX teams, product owners, and stakeholders to deliver scalable AI solutions • Ensure data quality, integrity, security, and performance across ingestion and retrieval systems • Implement monitoring, logging, and troubleshooting for AI and data processing workflows • Contribute to architecture decisions, technical documentation, and engineering best practices • Participate in agile pod-based development teams and continuous improvement initiatives

• Design and develop reusable, parameter-driven ingestion and transformation pipelines • Build and maintain medallion architecture solutions • Develop performant ELT workflows • Create and optimize PySpark notebooks and distributed processing jobs • Design dimensional data models • Implement data vault patterns • Optimize distributed SQL workloads • Implement CI/CD processes • Build monitoring, logging, and auditing solutions • Lead or contribute to cloud modernization initiatives