Principal Data Engineer AI

Overview At Cotiviti, we are custodians of data for our clients. Principal Data Engineers establish the data foundation of Cotiviti’s clinical AI platform—the pipelines, infrastructure, and clinical data models that make production-grade AI systems possible in a regulated healthcare environment. In this role, the Principal Data Engineer sets technical direction for data architecture, engineering, quality, and security across current and future clinical AI initiatives, ensuring the efficient and compliant execution of processes that manage data ingestion, production, quality, and the protection of protected health information (PHI). They serve as the senior technical authority for the group’s data management functions and mentor other engineers.  Responsibilities 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.  Complete all responsibilities as outlined in the annual performance review and/or goal setting.   Complete all special projects and other duties as assigned.   Must be able to perform duties with or without reasonable accommodation.   This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change.   Qualifications 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  Experienced leveraging AI for enhancing engineering productivity with tools like Claude Code, CoPilot, MCP capabilities etc.  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.  Deep experience building and operating data systems in regulated environments (healthcare, financial services, or similar), including audit trail design, compliance-grade change management, and formal validation processes.  Demonstrated experience mentoring senior engineers and shaping technical direction at organizational scale.  Cognitive/Mental Requirements:   Communicating with others to exchange information.  Problem-solving and thinking critically.  Completing tasks independently.  Interpreting data.  Making timely decisions in the context of a workflow.  Maintaining