Staff Data Engineer

CVS Health•York, PA

2d•$106,605 - $260,590

About The Position

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 The Staff Data Engineer will design, build, and support a near real-time transactional cache platform that enables APIs, digital experiences, and downstream services to consume operational data outside of the legacy UniVerse environment. The role is responsible for the end-to-end data flow from UniVerse through CDC replication into PostgreSQL, transformation and enrichment processes on GCP, and delivery of optimized data models within MongoDB Atlas. The ideal candidate will possess deep expertise in operational data platforms, CDC technologies, data replication, PostgreSQL, MongoDB, and cloud-native data engineering. This individual will serve as a technical leader responsible for architecture, implementation, performance optimization, reliability, and operational support of the transactional cache ecosystem. The platform is intended to reduce transactional workloads on UniVerse while providing scalable, near real-time access for APIs and services.

Requirements

7+ years of data engineering, software engineering, or data platform development experience.
5+ years designing and implementing enterprise-scale data platforms and distributed systems.
Experience with UniVerse or UniData operational databases.
Proficiency in PICK BASIC, wIntegrate, Unix/Linux, and SDLC methodologies
Strong experience with Change Data Capture (CDC), replication technologies, and near real-time data movement patterns.
Experience designing operational data stores, transactional caches, or API-serving data platforms.
Strong Python and SQL development skills.
Experience developing and supporting production ETL/ELT pipelines.
Experience with Linux/Unix environments.
Ability to troubleshoot distributed data processing and data synchronization issues.
Strong architecture, communication, and technical leadership skills.

Nice To Haves

Experience with GCP services including Cloud SQL, Dataproc Serverless, Cloud Composer (Airflow), Monitoring, IAM, and cloud networking fundamentals.
PostgreSQL expertise including schema design, query optimization, indexing, performance tuning, high availability concepts.
MongoDB Atlas expertise including data modeling, aggregation frameworks, index design, performance optimization.
Experience implementing transactional cache or operational data platform architectures.
Kafka, Pub/Sub, or event-driven architecture experience.
Experience building data platforms that support APIs and microservices.
Terraform or Infrastructure as Code experience.
Experience with GKE/Kubernetes.
Experience designing highly available and low-latency data platforms.
Agile/SAFe experience in large enterprises.
Healthcare experience.
Cloud certifications (AWS/GCP) preferred.

Responsibilities

Lead the design and implementation of a near real-time transactional cache platform supporting APIs, applications, and digital experiences.
Design and support Change Data Capture (CDC) pipelines that replicate data from UniVerse into PostgreSQL using vendor-supported replication technologies.
Develop and maintain transformation pipelines that convert operational relational data into optimized MongoDB document structures.
Design and evolve MongoDB document models, indexing strategies, partitioning approaches, and query patterns to support high-throughput, low-latency API consumption.
Partner with API and application development teams to define and deliver cache-ready domain models that meet performance and scalability requirements.
Establish data freshness, latency, monitoring, and availability standards for transactional cache workloads.
Optimize PostgreSQL and MongoDB performance through schema design, indexing, query tuning, and capacity planning.
Own platform reliability, observability, troubleshooting, incident response, and operational support for production transactional cache environments.
Develop operational runbooks, monitoring dashboards, alerting strategies, and support procedures for production cache environments.
Provides technical leadership across multiple engineering teams, influences architecture decisions, establishes engineering standards, and drives strategic modernization initiatives.
Mentor engineers and establish engineering standards for CDC, operational data pipelines, and cloud-native data platforms.