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.

CVS Health is seeking a highly skilled and driven individual contributor to join our Enterprise Observability and Security Engineering team. This role focuses on building, scaling, and operationalizing the enterprise observability lakehouse that powers threat detection, incident response, and platform visibility across hybrid cloud environments.

The ideal candidate will demonstrate deep expertise in Databricks, large-scale telemetry processing, and OTEL-aligned observability architectures. This position requires strong engineering rigor, the ability to design high-volume log, metric, and trace pipelines, and a passion for improving security and reliability through data. A critical aspect of the role includes partnering with Security Engineering, SRE, and Cloud teams to ensure telemetry is complete, trustworthy, and actionable.

• Build, scale, and maintain enterprise-grade log, metric, and trace pipelines using Databricks, cloud data lakes, and distributed data processing engines.
• Implement ingestion and transformation workflows using Cribl, Vector, Git Hub Actions, Jenkins, or similar technologies.
• Design and expand an Observability Lakehouse aligned to Open Telemetry (OTEL) data models and standards.
• Normalize and model high-volume security and observability data for detection, forensics, and operational intelligence use cases.
• Develop automated ETL/ELT frameworks, Delta Lake architectures, and data quality checks for unstructured and semi-structured telemetry.
• Collaborate closely with Security Engineering, SRE, Cloud, and SOC teams to enhance enterprise visibility and improve detection fidelity.
• Build CI/CD workflows and reusable IaC-driven patterns for pipeline deployment and automation.
• Troubleshoot performance bottlenecks and drive continuous improvements in reliability, latency, and cost efficiency.
• Contribute to team knowledge sharing and engineering standards focused on observability, security, and reliability.

• 5+ years of experience building or supporting log, metric, or trace pipelines aligned to OTEL or similar telemetry standards in a Data, Security Data, or Observability Engineering level role.
• 5+ years of hands‑on experience with Databricks, Spark, or large-scale distributed data processing systems.
• 5+ years of experience working with cloud services across AWS, Azure, or GCP (storage, eventing, compute, or equivalent).
• 5+ years of experience with SQL and Python in production data environments.

• Experience with Cribl, Vector, Kafka, or similar high-volume ingestion technologies.
• Background supporting SIEM/SOAR, detection engineering, or threat analytics platforms.
• Familiarity with Delta Lake, Unity Catalog, metadata management, and lineage tooling.
• Understanding of enterprise observability platforms (Splunk, Datadog, Elastic, etc.).
• Knowledge of security governance, auditing, access controls, and sensitive-data handling.
• Experience with IaC tooling (Terraform, ARM/Bicep, Cloud Formation).
• Familiarity with cloud orchestration technologies (Azure Functions, AWS Lambda, GCP Cloud Functions, Logic Apps, Kubernetes-based platforms).
• Strong communication skills for both deeply technical and executive audiences.
• Passion for observability, security, continuous learning, and platform-level engineering.

Bachelor's degree from an accredited university or equivalent work experience (HS diploma + 4 years relevant experience).

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