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About This Role
Overview:
We are seeking a seasoned Director of Data Engineering to lead and scale a high\-performing, enterprise\-grade data engineering organization for a healthcare company with petabytes of data. This role is responsible for architecting and governing the data infrastructure and drive the technical roadmap across foundational infrastructure, streaming pipelines, analytics engineering, and AI enablement, and serve as a strategic partner to product, analytics, clinical informatics, and technology leadership
Responsibilities:
- Oversee the data engineering efforts in ensuring that Cotiviti is able to provision scalable, reliable data pipelines from the various backend data sources to the cloud platform, applying best\-in\-class cloud\-native technologies (AWS, Azure, or GCP) and modern frameworks including Spark, Kafka, dbt, Databricks, and the Cloudera/Hadoop ecosystem.
- Lead and manage a team of data engineers in delivering quality code and building data products and use cases, fostering a culture of engineering excellence, accountability, and continuous improvement across multiple functional squads.
- Ensure the organization follows best practices in data architecture and engineering standards so that pipelines are established with reusable, scalable engineering patterns aligned to data mesh principles and enterprise platform engineering.
- Influence the vision for the data engineering platform in creating data products that power multiple use cases and projects — including AI/ML use cases, Analytics and BI use cases, and LLM\-powered Agentic use cases — collaborating closely with Architecture and Product Management teams to shape the data product roadmap.
- Establish a metrics\- and KPIs\-driven approach to measure the work done by the data engineering team, track business value of data products created, and monitor, measure, and continuously improve team performance and throughput.
- Influence solution architecture of AI/ML and Agentic AI solutions, collaborating with Data Science, Product, and line\-of\-business teams to ensure data infrastructure is AI\-ready, including feature store availability, embedding pipelines, vector databases, and real\-time serving capabilities.
- Ideate and lead Proof of Concepts and MVPs that advance Cotiviti's data engineering posture in the rapidly evolving landscape of AI, generative AI, and agentic systems.
- Identify and monitor key business risks related to realizing the data needs of the business, communicating risks and mitigation strategies proactively to VP\- and C\-suite stakeholders.
- Evangelize strong data engineering patterns and practices throughout the organization by communicating the vision and use cases of advanced analytics, data products, and AI\-enabled capabilities to both technical and non\-technical audiences.
- Hire, develop, coach, lead, and retain top\-tier talent, with a focus on building and improving a team and culture that employs best\-in\-class practices to drive high levels of internal and external customer satisfaction.
- 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 and 12\+ of professional experience required in a data engineering, analytical, or information specialist role within a corporate or consulting setting; experience in healthcare, life sciences, or a related industry strongly preferred
- Deep understanding of data engineering architectures, technologies, and platforms focused on large\-scale data management and AI applications, including cloud\-native platforms such as Databricks, GCP, and AWS, as well as the Cloudera/Hadoop ecosystem
- Strong foundation and point of view on generative AI techniques like LLMs and Agentic architectures in solving data engineering problems
- Proven expertise in designing and implementing enterprise data architectures on cloud platforms, with hands\-on fluency in ETL/ELT, data modeling, data integration, and the modern data stack (Spark, dbt, Kafka, Airflow)
- Deep understanding of data governance, data quality, and data security best practices, including HIPAA compliance and handling of sensitive healthcare and claims data in a discretionary manner
- Strong foundation and clear point of view on generative AI techniques — including LLMs and Agentic architectures — and how they apply to solving data engineering problems at enterprise scale
- Demonstrated ability to solve complex business problems by creating scalable data products that surface insights from both structured and unstructured data, including the ability to create examples, prototypes, and demonstrations that help leadership better understand the work
- Proven ability to lead and manage large data engineering teams (15\+ Data engineers), including hiring, developing, coaching, and retaining top\-tier talent with a focus on high performance and customer satisfaction
- Proficiency at planning and setting meaningful objectives aligned to organizational goals; ability to articulate, promote, and implement strategic plans while managing multiple concurrent projects, shifting priorities, and firm deadlines
- Exceptional written, verbal, and interpersonal communication skills with the ability to identify and articulate business challenges, project objectives, and engineering approaches to both technical and non\-technical audiences, including executive stakeholders
- Strong initiative, self\-motivation, and ability to work autonomously while also leading and collaborating within large cross\-functional teams in a customer\-service\-oriented environment
Cognitive/Mental Requirements* Problem\-solving and thinking critically.
- Completing tasks independently.
- Interpreting data.
- Maintaining focus.
Physical Requirements and Working Conditions:* 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.
Base compensation ranges from $190,000 to $225,000 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: 6/08/2026
Applications are assessed on a rolling basis. We anticipate that the application window will close on 09/08/2026, 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 $190K-$225K range is above the 75th percentile for Data Engineer roles in our dataset (median: $168K across 38 roles with salary data).
Role Details
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 4,133 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
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 $208,300 based on 273 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. Disclosed range: $190K to $225K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Cotiviti AI Hiring
Cotiviti has 9 open AI roles right now. They're hiring across Research Engineer, Data Engineer, AI Software Engineer, AI/ML Engineer. Based in Remote, US. Compensation range: $54K - $258K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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
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