Senior Manager, Data Engineering & AI

CVS Health•Irving, TX

19h•$118,450 - $284,280

About The Position

The Senior Manager, Data Engineering & AI is a people and technical leader responsible for a team of software and data engineers, including employees and contractors. This player-coach role devotes approximately 50% of its time to hands-on design, coding, prototyping, architecture, and solving complex technical problems, with the remaining time focused on team leadership, cross-functional partnership, and strategy. The role owns the team’s growth, performance, and delivery, and works across engineering, product, data science, and business functions to turn ideas into production-grade solutions that deliver measurable outcomes. The position leads the design and delivery of data and AI-enabled platforms and products, including GenAI, RAG, and LLM-based solutions. It establishes engineering practices and architecture, develops roadmaps aligned with business and technology objectives, and supports the productionization of models and prototypes. Healthcare experience and contact center analytics engineering are highly valued. The role requires a master’s degree in Computer Science, Engineering, or a related field. The typical pay range is $118,450.00–$284,280.00, with eligibility for a CVS Health bonus, commission, or short-term incentive program and an award target in the company’s equity award program. This full-time position is eligible for benefits including medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility. The anticipated application window closes 02/05/2027.

Requirements

At least 8 years of experience in data engineering, software engineering, or a related field.
At least 3 years of people management experience, including hiring, performance management, and developing engineers.
Demonstrated ability and willingness to spend approximately 50% of work time on hands-on design and development.
Deep understanding of full-stack engineering, software engineering principles, design fundamentals, and first-principles problem solving.
Strong proficiency in Python and SQL, plus at least one additional language such as Java, Scala, or JavaScript/TypeScript.
Experience designing and building data pipelines, data models, and data platforms on GCP, AWS, or Azure.
Hands-on experience applying AI/ML or GenAI in production data environments.
Experience collaborating with product, data science, and business teams to deliver outcomes.
Strong knowledge of CI/CD, testing, observability, and DevOps practices for data and software systems.
Ability to plan, deliver, and support work effectively.
Ability to work with business intelligence and translate business needs into technical solutions.
Strong collaboration, teamwork, and relationship-building skills across organizational boundaries.
Mastery of problem solving and decision making.
Growth mindset, including agility and developing oneself and others.
Excellent written and verbal communication skills and ability to influence senior stakeholders.
Master’s degree in Computer Science, Engineering, or a related field.

Nice To Haves

Experience in healthcare data environments, including familiarity with HIPAA, PHI handling, and healthcare data standards.
Experience with contact center analytics engineering, including call transcription, speech and text analytics, call driver or reason classification, and customer experience metrics.
Hands-on experience with LLM application frameworks such as LangChain or LangGraph, vector databases, and managed AI platforms such as Vertex AI.
Experience with data governance, data quality, lineage, and access control practices.
Experience managing blended teams of employees and contractors, including managers or team leads.
Familiarity with engineering performance measurement frameworks such as DORA.
Track record of driving innovation from idea through proof of concept to production adoption.

Responsibilities

Manage a team of software and data engineers, including hiring, onboarding, goal setting, performance management, career development, and retention.
Coach, mentor, and provide feedback and recognition to engineers; address performance concerns and identify skill-development opportunities.
Lead a blended team of employees and contractors, establish clear expectations and accountability, and foster a cohesive team culture.
Plan team capacity and priorities while balancing delivery commitments, technical debt, and individual growth.
Spend approximately 50% of work time designing, coding, prototyping, and troubleshooting data pipelines, services, and AI-enabled applications.
Author reference implementations, establish engineering patterns, conduct code reviews, enforce coding standards, and resolve complex technical challenges.
Define software development lifecycle strategy and develop architecture for data pipelines, interfaces, and analytics initiatives.
Develop and execute roadmaps for data-driven and AI-enabled software projects aligned with business objectives and technology vision.
Evaluate and adopt industry-standard data and advanced analytics capabilities, leveraging existing services and identifying leading solutions.
Identify and mitigate technical risks that could affect project delivery, data quality, or data integrity.
Lead design and delivery of AI/ML-enabled data products, including GenAI, RAG, and LLM solutions, and supporting data foundations, MLOps/LLMOps practices, and evaluation frameworks.
Build scalable, reliable, and cost-efficient platforms and pipelines for batch and real-time data processing.
Partner with product managers and UX/UI designers to define requirements, shape roadmaps, prioritize work, and translate needs into technical specifications and deliverables.
Work with data scientists and analytics teams to productionize models and prototypes into secure, observable, scalable systems and provide supporting data foundations.
Build relationships with business stakeholders, understand priorities, shape data and AI investments, and demonstrate measurable value.
Communicate technical and analytical results clearly to technical and non-technical leaders.
Generate and champion ideas, run proofs of concept and experiments, and scale successful solutions.
Stay current on AI and data engineering technologies and provide pragmatic recommendations to the team and leadership.