Senior Data Engineer

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 Senior Data Engineer will design, build, and support data engineering solutions on Google Cloud Platform, with a focus on scalable data pipelines, database integration, and ETL processes that enable analytics, reporting, and downstream application consumption. The role is responsible for data ingestion and transformation using GCP-native services such as BigQuery, Spanner, and Dataflow, alongside integration with enterprise databases including Oracle, PostgreSQL, and SQL Server.

The Senior Data Engineer will possess strong hands-on experience with GCP data and compute services, relational and distributed databases, ETL tooling, and Unix/Linux environments. This individual will contribute to the design, development, performance tuning, and operational support of data platforms, working closely with senior engineers and architects to deliver reliable, efficient, and well-monitored data solutions.

Expectations for the Role

• Design, develop, and maintain data pipelines using GCP services including BigQuery, Cloud Spanner, and Dataflow for batch and streaming data processing
• Build and support ETL/ELT workflows that extract, transform, and load data across Oracle, Spanner, SQL Server, PostgreSQL, and other relational databases
• Develop and optimize BigQuery datasets, tables, partitioning, and clustering strategies to support efficient querying and reporting
• Implement and tune Dataflow pipelines for data transformation, enrichment, and movement between source systems and target data stores
• Monitor and manage Compute Engine VMs, including CPU utilization, resource allocation, and performance tuning across GCP workloads
• Support and troubleshoot other core GCP technologies such as Cloud Storage, Cloud Composer, Pub/Sub, and IAM as part of end-to-end data workflows
• Apply data warehousing concepts including dimensional modeling, star/snowflake schemas, slowly changing dimensions, and data mart design
• Develop and support ETL workflows using tools such as Informatica, integrating with GCP and on-premise database systems
• Work extensively in Unix/Linux environments including shell scripting, file system navigation, permissions management, and log/file handling
• Schedule, monitor, and troubleshoot batch jobs using Control-M, ensuring timely and reliable execution of data pipelines
• Participate in Agile/JIRA-based 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 multiple platforms
• Troubleshoot data pipeline failures, performance bottlenecks, and data quality issues across the stack
• Document technical designs, operational runbooks, and support procedures for the platforms being built
• Stay current with emerging AI/ML and GenAI tools and proactively apply them to improve productivity, automate repetitive tasks, and streamline development and data engineering workflows

REQUIRED QUALIFICATIONS

• 5+ years of experience in data engineering, software engineering, or database development
• 3+ years of hands-on experience with Google Cloud Platform (GCP) products, especially BigQuery, Cloud Spanner, and Dataflow
• Strong experience working with multiple database platforms, especially Oracle, Spanner, SQL, and PostgreSQL
• Working knowledge of Google ETL technologies and other core GCP services (Compute Engine/VMs, Cloud Storage, Cloud Composer, Pub/Sub)
• Experience with performance tuning, including CPU utilization analysis and resource optimization on GCP compute resources
• Solid understanding of data warehousing concepts (dimensional modeling, fact/dimension tables, 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/optimize complex queries

PREFERRED QUALIFICATIONS

• Good troubleshooting, communication, and collaboration skills
• Awareness of AI/GenAI tools and techniques, with a willingness to continuously learn and upskill to adopt new 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

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