Staff Systems Engineer - Digital

CVS Health•Work At Home-Arizona, AZ

4d•$118,450 - $236,900

About The Position

CVS Health is seeking a Staff Systems Engineer to own and evolve shared data infrastructure used across digital products. The role works horizontally with engineering, product, platform, analytics, and AI teams, and serves as the primary authority on modeling, governing, and serving information across operational, analytical, and AI workloads. It leads architecture decisions and foundational data components with an emphasis on quality, compliance, reliability, and enabling teams across the company. The engineer defines enterprise data architecture and platform standards, establishes governance and compliance frameworks, and designs data capabilities for AI/ML and advanced analytics. The role also advises teams on complex decisions, promotes consistent standards and adoption through documentation and onboarding materials, and establishes quality, observability, testing, and data reliability practices. This is a full-time position. The typical pay range is $118,450 to $236,900. The position is eligible for a CVS Health bonus, commission or short-term incentive program and an equity award target. Benefits include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, subject to eligibility.

Requirements

At least 7 years of experience in data engineering, data architecture, or related roles.
At least 5 years of hands-on experience with cloud-native data platforms, including GCP (BigQuery, Dataflow, Pub/Sub) and Azure Synapse.
At least 5 years of experience with data governance, compliance, and regulatory requirements.
Bachelor's degree or equivalent experience; equivalent experience is specified as a high school diploma plus 4 years of relevant experience.
Ability to think in systems and understand how data decisions affect platforms, teams, and downstream consumers.
Clear written and verbal communication across engineering, product, and executive audiences.
Proactive identification of data quality and architecture risks.
Ownership of architecture decisions from design through documentation and adoption.
Collaborative approach that helps improve data maturity across teams.

Nice To Haves

Track record of building and governing enterprise-scale data platforms across multiple product teams.
Ability to communicate complex data architecture decisions to technical and non-technical stakeholders.
Expertise in relational and non-relational data modeling, including dimensional modeling, Data Vault, or event-sourced patterns.
Strong SQL skills and proficiency in at least one data pipeline language, such as Python, Scala, or Spark.
Experience with streaming data architectures such as Kafka, Pub/Sub, or Kinesis.
Experience with data governance tools such as Dataplex, Collibra, or Alation.
Familiarity with data mesh principles and federated data ownership models.
Knowledge of feature store platforms such as Feast, Tecton, or Vertex AI Feature Store.
Experience with dbt, Great Expectations, or similar data transformation and quality frameworks.
Exposure to LLM data pipelines, including RAG architectures, embedding generation, or vector database design with tools such as Pinecone or Weaviate.
Experience supporting or leading data platform or data foundation teams in a multi-team organization.

Responsibilities

Define and own 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, including data lakes, warehouses, streaming, and serving layers.
Serve as the primary point of contact and subject-matter expert 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 for 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 with regulatory and business requirements.
Design data architectures for 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 support LLM and generative AI use cases.
Establish MLOps-adjacent data patterns, including dataset versioning, training/serving skew detection, and model input monitoring.
Partner 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 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, including schema validation, freshness SLAs, completeness checks, and anomaly detection.
Define data pipeline observability standards, including alerting, lineage tracking, and incident response.
Drive data reliability engineering practices to minimize data incidents and reduce mean time to resolution.
Champion unit, integration, and contract testing for data pipelines.