Senior Data Engineer

Ceresti Health is a tech-enabled dementia care provider aiming to enhance care for people living with dementia. As a Senior Data Engineer, you will design and manage data architecture, ensuring data quality and accessibility for analytics and AI models, ultimately supporting the mission to improve care outcomes for patients and their families.

Responsibilities:

• 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

Requirements:

• BS/BA degree or higher in Computer Science, Engineering, or a related technical field
• 8+ years of professional data engineering experience, with a track record of shipping production data systems end-to-end
• Mastery of PostgreSQL: schema design, indexing, query tuning, partitioning, logical replication, JSONB, extensions (pg_partman, pg_cron, pgvector, etc.), and operating Postgres at scale
• Strong experience designing and operating data pipelines, including file-based ingestion (SFTP / object storage drops) and API-based ingestion (REST, webhooks)
• Hands-on experience with one or more cloud platforms (AWS preferred) and their data primitives: object storage (S3), managed Postgres
• Experience designing data warehouses and/or data lakes and the judgment to know which one a given problem actually needs
• Strong experience with dbt (or equivalent SQL-based transformation framework) and modern data modeling patterns (Kimball dimensional, Data Vault, One Big Table — and an opinion about when each is right)
• Experience with at least one orchestration framework (Dagster, Prefect, or Airflow) and a clear point of view on which to use when
• Strong Python skills for ingestion, validation, and tooling
• Experience with data validation and data-quality frameworks (Great Expectations, Pandera, Soda, or equivalent)
• Experience with change-data-capture from Postgres (logical replication, or equivalent)
• Data governance experience in a HIPAA-regulated environment or, at minimum, demonstrated instincts for protecting PHI and PII (encryption, least privilege, audit, de-identification, BAA-aware vendor selection); HITRUST or SOC 2 experience is a strong plus
• Comfortable with infrastructure-as-code and CI/CD for data systems
• Experience supporting ML workloads: building feature tables, managing training data, serving features at inference time; familiarity with embeddings, vector search (pgvector or equivalent), and LLM integration patterns (RAG, prompt-grounded analytics) is a plus
• Experience using AI coding assistants (e.g., GitHub Copilot, Cursor, Claude) to accelerate development
• Excellent written and verbal communication skills: you can explain a tricky schema decision to a business stakeholder and a data contract to a partner with equal clarity
• Demonstrated experience working in Agile/Scrum teams
• Reliable, persistent and results-oriented
• Easy to get along with; able to work with a team
• Must demonstrate a high level of integrity and ownership
• Consistently transparent, courageous and enthusiastic
• Bias toward simplicity: you can recite the trade-offs of the heavyweight modern data stack and still default to the smallest thing that works
• Must be able to pass a background check