Data Engineer

CVS Health•Washington, NV

2d•$64,890 - $173,040•Remote

About The Position

We are hiring a fully remote Data Engineer with strong SQL and Python experience to join our Data Engineering team. You will contribute to the design, development, and maintenance of data pipelines and platform infrastructure that power a new internal self-service application. Working alongside senior engineers, you will help modernize how data is ingested, transformed, and served — enabling application teams across the company to own and manage their data with greater speed and reliability. This is an opportunity to grow your data engineering skills on a high-visibility platform initiative with direct impact on data quality and accessibility. This is a fully remote position open to candidates anywhere in the US. This role is well-suited for a detail-oriented engineer who takes pride in building reliable, well-tested data pipelines and is eager to deepen their expertise in modern data platforms. You will work with growing autonomy on well-defined tasks, collaborating closely with senior engineers who will provide guidance, mentorship, and code review. You are expected to ask good questions, communicate blockers promptly, and take ownership of your contributions from implementation through production monitoring. You will have access to AI development tools — including Claude, Snowflake CoCo, and Copilot — to assist across all phases of the SDLC. You will contribute to a platform that provides: Self-Service Data Ingestion — Build and maintain the pipelines that enable application teams to register Kafka topics, update schemas, and ingest data, reducing lead times from weeks to minutes. Automated Monitoring & Incident Management — Implement standardized logging, alerting, and escalation to improve pipeline reliability and issue resolution. Data Preview Capabilities — Support features that allow application engineers to preview how their data will appear in the warehouse before it reaches production. The Data Self-Service Platform addresses critical challenges in our current data operations. Today, data ingestion is often manual and ticket-driven, leading to delays and limiting self-service for application teams. You will help build a platform that empowers data owners to ingest, transform, and serve their data — giving them full control and accountability for data quality. This platform will enable data producers and consumers to collaborate efficiently, address issues directly, and streamline testing changes.

Requirements

2 years of professional data engineering or software development experience.
Strong, demonstrated proficiency in SQL — including writing, optimizing, and troubleshooting complex queries against large datasets.
Experience working with at least one cloud data warehouse or data platform (Snowflake, BigQuery, Amazon Redshift, Azure Synapse Analytics, or Databricks).
Proficiency with Git-based version control, including branching, pull requests, and code review workflows.
Familiarity with CI/CD concepts and participation in automated deployment workflows (GitHub Actions).

Nice To Haves

Hands-on experience building and maintaining data pipelines or ETL/ELT processes using Python.
Familiarity with Snowflake — any exposure to querying, schema concepts, or warehouse basics is a plus; it is a core platform tool for this team.
Experience writing unit and integration tests for data pipeline components.
Any exposure to DBT (Data Build Tool) or similar transformation frameworks; DBT is a key part of our data workflow and familiarity is a strong plus.
Familiarity with Kafka or similar event-streaming platforms (RabbitMQ, AWS SNS/SQS).
Familiarity with containerized development (Docker).
Basic exposure to Terraform or infrastructure-as-code concepts.
Awareness of structured logging, pipeline health checks, and monitoring/alerting tooling.
Familiarity with REST API integration patterns.
Experience with AI-enhanced development tools such as Claude, Snowflake CoCo, or Copilot.

Responsibilities

Build, maintain, and improve data pipelines and ETL/ELT processes using Python, contributing to reliability, scalability, and observability.
Write and optimize SQL queries to support data ingestion, transformation, troubleshooting, and validation across the data warehouse.
Contribute to data modeling efforts and help maintain the integrity and structure of datasets within the Snowflake data platform.
Integrate with Kafka-driven event streams to support real-time and near-real-time data ingestion under the guidance of senior engineers.
Write clean, well-tested code; ensure thorough unit and integration test coverage for pipeline components you build.
Participate in code reviews, giving and receiving constructive feedback to uphold team quality standards.
Contribute to CI/CD workflows using GitHub Actions and validate pipeline correctness post-deployment.
Implement logging and observability instrumentation for pipelines you own, and respond to production alerts as part of team on-call rotation.
Communicate blockers and progress clearly; escalate issues with appropriate urgency.
Leverage AI development tools (Claude, Snowflake CoCo, Copilot) to improve development speed and code quality.
Actively seek out mentorship and coaching from senior engineers; share learnings with teammates.