Senior Data Engineer

CVS Health•Work At Home-North Carolina, NV

1d•$83,430 - $222,480

About The Position

The Senior Data Engineer designs, builds, and supports data engineering solutions on Google Cloud Platform (GCP), focusing on scalable data pipelines, database integration, and ETL processes that support analytics, reporting, and downstream applications. The role uses GCP services including BigQuery, Cloud Spanner, and Dataflow, and integrates with enterprise databases such as Oracle, PostgreSQL, and SQL Server. The position contributes to the design, development, performance tuning, and operational support of data platforms. It works with senior engineers and architects to deliver reliable, efficient, and well-monitored solutions, and includes troubleshooting pipelines and data quality issues, documenting designs and operational procedures, and applying emerging AI/ML and GenAI tools to improve engineering workflows.

Requirements

At least 5 years of experience in data engineering, software engineering, or database development.
At least 3 years of hands-on experience with GCP products, especially BigQuery, Cloud Spanner, and Dataflow.
Strong experience with multiple database platforms, especially Oracle, Spanner, SQL, and PostgreSQL.
Working knowledge of Google ETL technologies and core GCP services, including Compute Engine/VMs, Cloud Storage, Cloud Composer, and Pub/Sub.
Experience with performance tuning, including CPU utilization analysis and resource optimization on GCP compute resources.
Solid understanding of data warehousing concepts, including dimensional modeling, fact and dimension tables, and ETL design patterns.
Experience with ETL tools such as Informatica for data integration and transformation.
Strong Unix/Linux experience, including shell scripting and file-system handling.
Experience with job scheduling tools such as Control-M.
Strong SQL development skills and ability to write and optimize complex queries.
Bachelor’s degree or equivalent experience; alternatively, a high school diploma and 4 years of relevant experience.

Nice To Haves

Good troubleshooting, communication, and collaboration skills.
Awareness of AI/GenAI tools and techniques, and willingness to learn and adopt technologies that improve engineering efficiency.
Experience with additional databases such as MongoDB, MySQL, or Cassandra.
Exposure to Cloud Composer (Airflow) for workflow orchestration.
Experience with Kafka, Pub/Sub, or event-driven data architectures.
Familiarity with Terraform or Infrastructure as Code practices.
Experience with GKE/Kubernetes.
Exposure to CDC (Change Data Capture) tools and replication technologies.
Experience in Agile/SAFe environments within large enterprises.
Healthcare or retail industry experience.
GCP certification, such as Professional Data Engineer.
Familiarity with Agile methodologies and JIRA for project tracking and development workflows.

Responsibilities

Design, develop, and maintain batch and streaming data pipelines using GCP services such as BigQuery, Cloud Spanner, and Dataflow.
Build and support ETL/ELT workflows that move and transform data across Oracle, Spanner, SQL Server, PostgreSQL, and other relational databases.
Develop and optimize BigQuery datasets, tables, partitioning, and clustering for efficient querying and reporting.
Implement and tune Dataflow pipelines for data transformation, enrichment, and movement between systems.
Monitor and manage Compute Engine virtual machines, including CPU utilization, resource allocation, and performance tuning.
Support Cloud Storage, Cloud Composer, Pub/Sub, and IAM within end-to-end data workflows.
Apply data warehousing concepts, including dimensional modeling, star and snowflake schemas, slowly changing dimensions, and data mart design.
Develop and support Informatica ETL workflows integrating GCP and on-premise database systems.
Work in Unix/Linux environments, including shell scripting, file-system navigation, permissions management, and log and file handling.
Schedule, monitor, and troubleshoot batch jobs using Control-M.
Participate in Agile/JIRA development processes, including sprint planning, ticket tracking, status updates, and defect management.
Collaborate with senior engineers and architects on performance tuning, query optimization, and database design across platforms.
Troubleshoot data pipeline failures, performance bottlenecks, and data quality issues.
Document technical designs, operational runbooks, and support procedures.
Stay current with AI/ML and GenAI tools and apply them to improve productivity, automate repetitive tasks, and streamline development and data engineering workflows.