Senior Data Engineer

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Ceresti Health

Site WebLinkedInTous les Emplois

11 - 50 employés

Fondée en 2013

🏥 Santé

⚕️ Assurance santé

🤝 B2B

🔥 Financement dans la dernière année

💰 €11 726 799 Venture Round - Ceresti Health en 2025-08

Healthcare • Healthcare Insurance • B2B

Ceresti Health est une entreprise de soins habilitée par la technologie qui collabore avec des mutuelles de santé et des organisations de soins responsables (ACOs) pour soutenir les aidants familiaux des personnes atteintes de démence. L'entreprise combine la stratification des risques basée sur les données de réclamations, l'éducation des aidants, une plateforme sur tablette et des navigateurs de soins personnels hebdomadaires pour améliorer l'adhérence aux plans de soins, combler les lacunes des déterminants sociaux de la santé et détecter de manière proactive les changements dans l'état des patients. Le programme dirigé par les aidants de Ceresti a des résultats validés de manière indépendante montrant des réductions substantielles des hospitalisations évitables et des coûts médicaux par patient, et il propose des contrats sans risque basés sur les résultats pour les payeurs.

Description

• 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

🎯 Exigences

• 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 • 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

🏖️ Avantages

• Competitive salary and benefits package • Opportunities for professional growth and development • Collaborative and dynamic work environment • Flexible work arrangements and remote work options • Access to cutting-edge technologies and tools