Staff Systems Engineer - Digital

CVS Health; Arizona; Hybrid; USD 118,000 - 237,000; Full time

Job summary

CVS Health is seeking a Staff Systems Engineer - Digital to own and evolve the shared data infrastructure powering access, governance, and intelligence across digital products. You will define enterprise data architecture, govern data models and contracts, and align platform tooling across product teams to ensure quality, compliance, and reliability at scale.

You will lead cross-functional collaboration with engineering, product, and AI teams, shaping data patterns for AI/ML workloads, feature

Qualifications

• 7+ years of experience in data engineering, data architecture, or related roles.
• 5+ years of hands-on experience with cloud-native data platforms - GCP (BigQuery, Dataflow, Pub/Sub), Azure Synapse
• 5+ years of experience with data governance, compliance, and regulatory requirements
• Strong communication skills, able to translate complex data architecture decisions for technical and non-technical stakeholders
• Relational and non-relational data modeling expertise - dimensional modeling, Data Vault, or event-sourced patterns
• SQL and at least one data pipeline language (Python, Scala, or Spark)
• Experience with streaming data architectures - Kafka, Pub/Sub, Kinesis
• Experience with data governance tooling - Dataplex, Collibra, Alation
• Familiarity with data mesh principles and federated data ownership models
• Knowledge of feature store platforms (Feast, Tecton, Vertex AI Feature Store) for ML use cases
• Experience with dbt, Great Expectations, or similar data transformation and quality frameworks
• Exposure to LLM data pipelines - RAG architectures, embedding generation, or vector database design (Pinecone, Weaviate)
• Experience supporting or leading data platform or data foundation teams in a multi-team organization

Responsibilities

• 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)
• 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
• 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
• 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
• 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
• 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 resolution
• Champion testing practices for data pipelines - unit, integration, and contract testing

Skills

Data governance

Data architecture

Data modeling

SQL

Python

Scala

Spark

Cloud data platforms

Communication skills

LLM data pipelines

Education

Bachelor's degree or equivalent experience

Tools

Dataplex

Collibra

Alation

Kafka

Pub/Sub

Kinesis

dbt

Great Expectations

Job description

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 resolution
• Champion testing practices for data pipelines - unit, integration, and contract testing

What We're Looking For:

• Beyond technical skills, we are looking for someone who:
• Thinks in systems - you see how data decisions ripple across platforms, teams, and downstream consumers
• Communicates with clarity - written and verbal, across engineering, product, and executive audiences
• Is proactive - you identify data quality and architecture risks before they become production incidentsTakes ownership - you see architecture decisions through from design to documentation to adoption
• Is collaborative by nature - you raise the data maturity of teams around you, not just your own

Required Qualifications:

• 7+ years of experience in data engineering, data architecture, or related roles
• 5+ years of hands-on experience with cloud-native data platforms - GCP (BigQuery, Dataflow, Pub/Sub), Azure Synapse
• 5+ years of experience with data governance, compliance, and regulatory requirements

Preferred Qualifications:

• Proven track record of building and governing enterprise-scale data platforms across multiple product teams
• Strong communication skills - able to translate complex data architecture decisions for technical and non-technical stakeholders
• Deep expertise in relational and non-relational data modeling - dimensional modeling, Data Vault, or event-sourced patterns
• Strong command of SQL and at least one data pipeline language (Python, Scala, or Spark)
• Experience with streaming data architectures - Kafka, Pub/Sub, Kinesis, or equivalent
• Experience with data governance tooling - Dataplex, Collibra, Alation, or similar
• Familiarity with data mesh principles and federated data ownership models
• Knowledge of feature store platforms (Feast, Tecton, Vertex AI Feature Store) for ML use cases
• Experience with dbt, Great Expectations, or similar data transformation and quality frameworks
• Exposure to LLM data pipelines - RAG architectures, embedding generation, or vector database design (Pinecone, Weaviate)
• Experience supporting or leading data platform or data foundation teams in a multi-team organization

Education:

• Bachelor's degree or equivalent experience (HS diploma + 4 years relevant experience)

Pay Range

The typical pay range for this role is:

$118,450.00 - $236,900.00

This pay range represents the base hourly rate or base annual fulltime salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above. This position also includes an award target in the company's equity award program.

Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong.

Great benefits for great people

We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families.

This fulltime position is eligible for a comprehensive benefits package designed to support the physical, emotional, and financial well-being of colleagues and their families. The benefits for this position include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility.

Additional details about available benefits are provided during the application process and on Benefits Moments.

We anticipate the application window for this opening will close on: 10/31/2026

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.