Staff Systems Engineer - Digital

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About the role

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary: We are looking for a Staff Systems Engineer - Digital to join our team, the foundational layer that powers access, governance, and intelligence across our digital products. You will work horizontally across engineering, product, and AI teams, leading, owning, and evolving the shared infrastructure that every team at the company depends on.   You will be the primary authority on how information is modeled, governed, and served across operational, analytical, and AI workloads - driving quality, compliance, and reliability at scale. If you thrive in a role where your architecture decisions multiply the productivity and capability of entire teams, this is the opportunity for you.   Key Responsibilities:   Data Architecture & Platform Ownership: • Define and own the enterprise data architecture strategy across operational, analytical, and AI/ML workloads • Design and govern data models, data contracts, and canonical schemas used across product and platform teams • Evaluate and standardize data platform tooling — data lakes, warehouses, streaming, and serving layers (GCP BigQuery, Pub/Sub, Dataflow, or equivalent) • Serve as the primary point of contact and SME for shared data platform concerns across teams • Lead technical design and solutioning for foundational data components and cross-cutting data concerns   Data Governance & Compliance: • Own data governance frameworks including data classification, lineage, ownership, and quality standards • Partner with legal, security, and compliance teams to ensure data handling meets HIPAA, CCPA, and applicable healthcare regulatory requirements • Define and enforce data access control patterns, masking strategies, and PHI handling across the platform • Drive data catalog adoption and metadata management practices across engineering and analytics teams • Establish data retention, archival, and deletion standards aligned to regulatory and business requirements   AI & Advanced Analytics Enablement: • Design data architectures that support AI/ML model training, feature engineering, and inference pipelines • Define feature store patterns and real-time data serving strategies for AI agent and recommendation systems • Partner with AI engineering teams to ensure data contracts and schemas are fit for LLM and generative AI use cases • Establish MLOps-adjacent data patterns — dataset versioning, training/serving skew detection, and model input monitoring   Cross-Team Collaboration & Enablement: • Partner closely with product engineering, platform, and analytics teams to understand data needs and deliver architectural solutions • Act as a technical advisor and escalation point for complex data architecture decisions across teams • Create and maintain clear documentation, data architecture decision records (ADRs), and onboarding guides for platform tools • Drive alignment on data standards, naming conventions, and shared data product strategies across teams   Quality, Observability & Reliability: • Establish data quality frameworks — schema validation, freshness SLAs, completeness checks, and anomaly detection • Define observability standards for data pipelines including alerting, lineage tracking, and incident response • Drive data reliability engineering practices that minimize data incidents and reduce mean time to resoluti

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