Principal Data Engineer AI

$175K - $205K Remote Senior Data Engineer

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Skills & Technologies

AwsClaudeGcpPython

About This Role

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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 focus.
  • Assessing the accuracy, neatness and thoroughness of the work assigned.
  • Applying established protocols in a timely manner

Working Conditions and Physical Requirements:* Remaining in a stationary position, often standing or sitting for prolonged periods.

  • Repeating motions that may include the wrists, hands and/or fingers.
  • Must be able to provide a dedicated, secure work area.
  • Must be able to provide high\-speed internet access / connectivity and office setup and maintenance.

No adverse environmental conditions expected.

*

Base compensation ranges from $175,000 to $205,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. This role is eligible for discretionary bonus consideration.

Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17\-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our Careers page.

Since this job will be based remotely, all interviews will be conducted virtually.

Date of posting: 7/24/2026

Applications are assessed on a rolling basis. We anticipate that the application window will close on 09/24/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected.

Salary Context

This $175K-$205K range is above the 75th percentile for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).

Role Details

Company Cotiviti
Title Principal Data Engineer AI
Location Remote, US
Category Data Engineer
Experience Senior
Salary $175K - $205K
Remote Yes

About This Role

Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.

The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.

Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Cotiviti, this role fits into their broader AI and engineering organization.

Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.

What the Work Looks Like

A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.

Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.

Skills Required

Aws (28% of roles) Claude (12% of roles) Gcp (15% of roles) Python (52% of roles)

SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.

AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.

Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.

Compensation Benchmarks

Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $175K to $205K.

Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.

Cotiviti AI Hiring

Cotiviti has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer, Data Engineer. Based in Remote, US. Compensation range: $129K - $260K.

Remote Work Context

Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.

Career Path

Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.

From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.

Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.

What to Expect in Interviews

Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.

When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.

AI Hiring Overview

The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.

The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).

Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.

The AI Job Market Today

The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 83 roles with disclosed compensation, the median salary for Data Engineer positions is $185,000. Actual compensation varies by seniority, location, and company stage.
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
About 15% of the 4,317 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Cotiviti is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from Data Engineer positions include Senior Data Engineer, ML Engineer, Data Platform Lead. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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