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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineering and AI Platforms - **Company:** CVS Health - **Location:** Cumberland, RI, United States - **Experience:** Expert - **Salary:** $130,295.0 - $260,590.0 - **Contract:** Permanent contract - **Skills:** Training Data, Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Automation of Tests, Microsoft Azure, BigQuery, Cloud Computing, Cloud Database, Cloud Engineering, Information Systems, Continuous Integration, Data as a Services, Data Architecture, Information Engineering, Data Governance, Data Systems, Data Warehousing, Distributed Computing Environment, Distributed Data Store, Event-Driven Programming, Data Flow Control, Data Intelligence, Python (Programming Language), Machine Learning, Metadata, Reference Data, Release Management, Cloud Services, DataOps, Cloudera, Search Technologies, Software Engineering, SQL Databases, Systems Integration, Workflow Management Systems, Google Cloud, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Snowflake, Apache Spark, Generative AI, Infrastructure as Code (IaC), Agentic-AI, Data Layers, Containerization, Data Lakes, AI Platforms, Pyspark, Infrastructure Automation Frameworks, Information Technology, Deployment Automation, Apache Kafka, Data Management, Machine Learning Operations, Video Streaming, Cloud Migration, Virtual Agents, Software Version Control, Data Pipelines, Devsecops, Databricks - **Published:** October 16, 2026 - **Apply:** https://www.careercircle.com/jobs/all/all/usa/ri/cumberland/defd8958-d5c5-4069-a028-6fbbfb94e0e2 ## About the Role * 8+ years of professional experience in data engineering, software engineering, data platforms, analytics engineering or a closely related discipline. * 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 with 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. Preferred Qualifications * 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., Bachelor's degree in computer science, engineering, information systems, data science or a related discipline, or equivalent professional experience. A master's degree is preferred. ## Description CI/CD PySpark DataOps Planning Auditing Coaching Dataflow Equities BigQuery Metadata DevSecOps AI Agents Vertex AI Operations Leadership Automation Innovation Resilience Data Lakes Compassion Databricks Warehousing Scalability Investments Reliability Prototyping Traceability Data Quality Data Science Apache Spark Apache Kafka Communication Observability Data Modeling Consolidation Accountability Prioritization Reconciliation Risk Awareness Data Pipelines Reference Data Responsible AI Building Trust Version Control Test Automation Microsoft Azure Access Controls Data Governance Maintainability Cloud Migration Computer Science Machine Learning Data Engineering Agile Methodology Incident Response Data Architecture Anomaly Detection Publish Subscribe Influencing Skills Quality Assessment Financial Services Workflow Management Business Priorities Amazon Web Services Information Systems Product Engineering Security Governance Time Off Management Software Engineering Production Readiness Technology Ecosystems Google Cloud Dataproc Google Cloud Composer Dependency Management Cloud-Native Computing Operational Excellence Distributed Data Store Artificial Intelligence Event-Driven Programming Infrastructure Automation SQL (Programming Language) Balancing (Ledger/Billing) Snowflake (Data Warehouse) Google Cloud Platform (GCP) Infrastructure as Code (IaC) Python (Programming Language) Continuous Improvement Process Retrieval Augmented Generation Generative Artificial Intelligence Frontline Decision-Making Autonomy MLOps (Machine Learning Operations) Artificial Intelligence Infrastructure, At CVS Health, we are building a world of health around every individual by creating a more connected, convenient and compassionate health experience. The Senior Data Engineering and AI Platforms will lead teams that develop secure, resilient and reusable data capabilities supporting analytics, operational decision-making and intelligent experiences. This leader will translate business priorities into scalable engineering roadmaps, guide cloud and data architecture, and establish disciplined practices for data quality, observability, model enablement and production operations. The role partners across product, architecture, security, governance, data science and business teams to deliver trusted outcomes while prioritizing safety, quality and responsible use of AI., Engineering Strategy and Platform Delivery * Set the technical direction and multi-quarter roadmap for enterprise data products, ingestion services, transformation frameworks and AI-ready data foundations. * Lead the design and implementation of batch, streaming and event-driven solutions across cloud data platforms, balancing performance, reliability, maintainability and cost. * Guide modernization of legacy integrations and reporting ecosystems into modular, cloud-native services with well-defined interfaces and 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 measurable adoption. AI/ML and Intelligent Data Products * 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 emerging AI-assisted development and agentic engineering approaches, introducing them where they improve delivery while maintaining enterprise controls. * Promote experimentation through prototypes and proofs of value, then define the engineering path required to scale successful concepts safely. Data Quality, Security and Operational Excellence * Embed automated validation, reconciliation, anomaly detection and data-quality controls across critical pipelines and data products. * Implement observability that provides timely insight into freshness, completeness, failures, service health and downstream impact. * Ensure solutions follow privacy, security, access-control, retention, audit and regulatory expectations applicable to healthcare and enterprise data. * 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. People Leadership and Cross-Functional Partnership * Lead, coach and develop data engineers and technical contributors; create clear 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 complex needs into prioritized capabilities, clear 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, ensuring deliverables meet engineering, security and operational expectations. Leadership Competencies * Translates strategy into an executable engineering plan and keeps teams focused on measurable business outcomes. * Makes sound decisions in ambiguous environments by combining technical depth, data, risk awareness and stakeholder context. * Develops talent through direct feedback, coaching, delegation and meaningful stretch assignments. * Builds trust across organizational boundaries and communicates effectively with both technical and non-technical audiences. * Challenges existing approaches constructively and creates reusable solutions rather than one-time implementations. * Maintains a strong customer, colleague and quality mindset throughout design, delivery and operations.