Real-Time Data Platform Lead (PostgreSQL & MongoDB)

CVS Health Corporation

Arizona

Hybrid

USD 107,000 - 261,000

Full time

5 days ago

Be an early applicant

Application generator

Turn this role into an interview — a resume and cover letter built around what this employer wants.

Get past ATS filters

Benefits offered by this job

Bonus eligibility

Equity awards

Comprehensive benefits

Job summary

CVS Health Corporation is seeking a Staff Data Engineer in Arizona to design, build, and support a near real-time transactional cache platform for APIs, apps, and services. You will lead architecture, implement CDC pipelines from UniVerse to PostgreSQL, transform data to MongoDB documents, and optimize performance with indexing and tuning while ensuring reliability and observability.

This role requires strong Python/SQL skills, 7+ years in data engineering, and a track record of delivering

Qualifications

7+ years in data engineering or related fields.
5+ years designing enterprise-scale data platforms and distributed systems.
Experience with UniVerse or UniData.
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.

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.