Senior Data Engineering and AI Platforms

CVS Health•Cumberland, RI

1d•$130,295 - $260,590

About The Position

At CVS Health, the Senior Data Engineering and AI Platforms leader develops secure, resilient and reusable data capabilities supporting analytics, operational decision-making and intelligent experiences. The role translates business priorities into scalable engineering roadmaps, guides cloud and data architecture, and establishes disciplined practices for data quality, observability, model enablement and production operations. It partners with product, architecture, security, governance, data science and business teams to deliver trusted outcomes while prioritizing safety, quality and responsible AI use. The role sets technical direction for enterprise data products, ingestion services, transformation frameworks and AI-ready data foundations. It leads batch, streaming and event-driven solution design across cloud data platforms, modernizes legacy integrations and reporting into modular cloud-native services, and establishes architecture guardrails. It oversees delivery from discovery through production readiness and adoption. The leader partners with data scientists, ML engineers and product leaders to operationalize predictive, natural-language and generative AI use cases using governed enterprise data. This includes enabling production ML and LLM workloads, applying responsible AI practices, evaluating AI-assisted development approaches and scaling successful prototypes safely. The position embeds data-quality controls and observability, ensures privacy, security and regulatory expectations are met, and defines service objectives, incident response and recovery practices. It drives DevSecOps and DataOps practices, leads and develops data engineers, builds an inclusive high-performing culture, and communicates delivery health, risks and investment choices to senior leaders.

Requirements

At least 8 years of professional experience in data engineering, software engineering, data platforms, analytics engineering or a closely related discipline.
At least 3 years of experience leading engineers, technical workstreams or cross-functional delivery for complex data solutions.
Demonstrated experience designing and operating cloud-based data pipelines, data lakes, warehouses or lakehouse platforms.
Strong programming capability in Python and SQL, with practical experience in distributed data processing such as Apache Spark or PySpark.
Experience with at least one major cloud ecosystem such as Google Cloud, Microsoft Azure or AWS, and modern data services such as BigQuery, Snowflake or Databricks.
Experience implementing automated deployment, testing, monitoring and operational controls for data workloads.
Working knowledge of machine-learning lifecycle concepts, including training data, feature preparation, model integration, evaluation, deployment and monitoring.
Proven ability to influence architecture and delivery decisions across product, engineering, security, governance and business stakeholders.
Bachelor's degree in computer science, engineering, information systems, data science or a related discipline, or equivalent professional experience.

Nice To Haves

Experience delivering data or AI capabilities in healthcare, financial services, retail, insurance or another regulated environment.
Hands-on familiarity with GCP services such as BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer and Vertex AI, or comparable services on Azure or AWS.
Experience with Kafka or other streaming technologies, container platforms, workflow orchestration and infrastructure-as-code practices.
Knowledge of MLOps, LLMOps, retrieval-augmented generation, embeddings, vector search, prompt evaluation or AI agent orchestration.
Familiarity with enterprise data governance, cataloging, lineage, master/reference data, semantic layers and role-based access controls.
Experience leading cloud migration, platform consolidation or modernization programs involving multiple source systems and downstream consumers.
Advanced degree in computer science, data science, engineering or a related field, or equivalent practical experience.
Relevant cloud, data engineering, Databricks, machine-learning or agile delivery certifications.
Master's degree preferred.

Responsibilities

Set technical direction and multi-quarter roadmaps for enterprise data products, ingestion services, transformation frameworks and AI-ready data foundations.
Lead design and implementation of batch, streaming and event-driven solutions across cloud data platforms.
Modernize legacy integrations and reporting ecosystems into modular cloud-native services with reusable engineering patterns.
Establish architecture guardrails for data modeling, storage, partitioning, orchestration, metadata, lineage and consumption layers.
Oversee delivery from discovery through production readiness, including estimation, dependency management, testing, release planning, support transition and adoption.
Partner with data scientists, ML engineers and product leaders to operationalize predictive, natural-language and generative AI use cases using governed enterprise data.
Enable production ML and LLM workloads through curated datasets, feature pipelines, retrieval patterns, evaluation data, model monitoring and repeatable deployment workflows.
Apply responsible AI practices, including traceability, human oversight, privacy safeguards, security review, explainability and ongoing quality assessment.
Evaluate AI-assisted development and agentic engineering approaches and introduce them where they improve delivery while maintaining enterprise controls.
Promote experimentation through prototypes and proofs of value, and define how successful concepts can be scaled safely.
Embed automated validation, reconciliation, anomaly detection and data-quality controls across critical pipelines and data products.
Implement observability for data freshness, completeness, failures, service health and downstream impact.
Ensure solutions follow applicable privacy, security, access-control, retention, audit and regulatory expectations.
Define service objectives, incident response practices, recovery procedures and continuous-improvement routines for production platforms.
Drive DevSecOps and DataOps practices through source control, infrastructure automation, CI/CD, automated testing and environment promotion standards.
Lead, coach and develop data engineers and technical contributors, creating accountability, growth plans and opportunities for broader ownership.
Build an inclusive, high-performing culture grounded in care, purposeful innovation, accountability, safety and quality.
Partner with business and technology stakeholders to turn needs into prioritized capabilities, acceptance criteria and transparent trade-off decisions.
Communicate architecture, delivery health, risks, dependencies and investment choices to senior leaders in concise business terms.
Coordinate internal teams, vendors and strategic partners to ensure deliverables meet engineering, security and operational expectations.