Principal Data Engineer, AI

🕒 il y a 9 jours

🇺🇸 États-Unis – Télétravail

💵 $175 000 - $205 000 / an

⏰ Temps Plein

🔴 Expert

🚰 Ingénieur Data

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

Postuler Maintenant

Trouver des Emplois à Distance Similaires

Configurer une alerte email pour les nouveaux emplois

📊 Vérifiez votre score de CV pour ce poste

Améliorez vos chances d'obtenir un entretien en vérifiant votre score de CV avant de postuler.

Télécharger le CV

Cotiviti

Site WebLinkedInTous les Emplois

5001 - 10000 employés

🏥 Santé

💼 Conseil

📦 Logistique

Healthcare • Consulting • Logistics

Cotiviti est une entreprise de technologie et d'analytique dans le secteur de la santé, spécialisée dans l'amélioration de l'exactitude des paiements et des performances grâce à des solutions avancées d'analytique de données. Elle collabore avec des régimes de santé, des agences gouvernementales et des prestataires de soins de santé pour fournir des insights qui améliorent la qualité et l'efficacité de la prestation de soins. Grâce à des solutions telles que l'ajustement des risques, la gestion des politiques de paiement et l'engagement des membres, Cotiviti vise à optimiser les résultats financiers et cliniques pour l'écosystème de la santé.

Description

• Set the technical direction, architecture standards, and engineering practices for the group’s clinical data platform, establishing the data foundation that makes production-grade AI systems possible in a regulated healthcare environment. • Own the protected health information (PHI) data pipeline end to end on AWS, including virtual private cloud (VPC) configuration, encryption, access controls, and audit logging. • Own the compliance-grade, immutable audit trail, using techniques such as chain hashes, write-once-read-many (WORM) storage, and conditional writes. • Own the data quality framework—the validation, evaluation, and analysis pipelines that measure completeness, freshness, drift, and correctness across all data sources. • Own the MLOps infrastructure that machine learning engineers depend on, including experiment tracking, model artifact storage, and deployment tooling. • Own data provenance management and maintain the common clinical data model used across current and future clinical AI projects. • Own the clinical knowledge graph—the canonical clinical fact representation that the policy rules engine reasons against and that future domain projects will consume. • Build and maintain the graph assembler that consumes natural language processing (NLP) output and produces schema-validated JSON, including intra-chart deduplication logic that reconciles multiple mentions of the same condition into a single authoritative node. • Evolve the clinical data schema across execution phases, from flat JSON, to a typed relational graph, to longitudinal and cross-encounter resolution. • Design and maintain graph updates as the clinical vocabulary expands and documentation updates are applied. • Influence technical practices and standards across the broader engineering organization, drawing from experience to raise the bar on how the team builds regulated systems. • Mentor senior and staff engineers, and shape hiring and technical growth for the data engineering function. • Represent the clinical data platform in cross-functional forums with product, clinical, compliance, and business stakeholders, and translate business requirements into technical architecture. • Serve as the technical escalation authority for complex data infrastructure and clinical data pipeline issues.

🎯 Exigences

• Bachelor’s degree in Computer Science, Information Technology or equivalent work experience • 12+ years of working knowledge of big data and cloud technologies, with primary depth in AWS (e.g., S3, Glue, Lambda, IAM, KMS, CloudTrail, EMR); working knowledge of GCP and Databricks a plus • Experience in implementing production data pipelines using SQL, Spark, and Python, with expertise in orchestration tools such as Airflow or Databricks Workflows • Experience or advanced familiarity dealing with Machine learning handoffs with Data engineering processes • 12+ years of data engineering experience, with strong exposure to healthcare data including enrollment, medical claims, and/or pharmacy claims • AWS, GCP, or Databricks certifications a plus • Clinical healthcare background—familiarity with clinical documentation, medical coding, and healthcare entities from a clinical perspective—is a strong plus • Experience with healthcare data interoperability standards (e.g., HL7, FHIR, C-CDA) and clinical terminologies (e.g., ICD-10, SNOMED CT, LOINC, RxNorm) is a strong plus • Experience building or maintaining knowledge graphs, clinical data models, or NLP-driven data pipelines is a plus • Experience with MLOps tooling and handling PHI in HIPAA-regulated environments is a strong plus • Deep data modeling expertise across relational, graph, and document paradigms. • Proven ability to communicate technical architecture and trade-offs clearly to executive stakeholders, engineering peers, and clinical subject matter experts.

🏖️ Avantages

• Medical, dental, and vision insurance coverage • Disability and life insurance coverage • 401(k) savings plans • Paid family leave • 9 paid holidays per year • 17-27 days of Paid Time Off (PTO) per year