This role pays less than
63%
of similar roles. Most pay
$134,599–$216,062
— the shaded band above.
At the midpoint, this role pays about
$158k
versus about
$175k
for comparable roles.
Based on 240 similar postings.
Employer
About CVS Health
CVS Health is a leading American healthcare company operating retail pharmacies, pharmacy benefit management services, and a health insurance segment through Aetna, one of the nation''s largest health insurers. Industry: Healthcare & Pharmacy
CVS Health currently has
98 open roles
on FindRole.
Listed pay typically runs
$106,605–$260,590
across 95 roles with salary data.
Senior Data Engineer, Observability Engineering joins the Enterprise Observability Platform organization to advance next-generation observability, infrastructure, and security data capabilities. This senior individual contributor role focuses on designing, building, and operating scalable data pipelines and products that power operational intelligence and security analytics. The successful candidate will develop Databricks-based solutions to ingest, transform, and govern high-volume telemetry, infrastructure, application, and security data. Key responsibilities include optimizing PySpark workloads, Structured Streaming jobs, and bronze/silver/gold architectures while ensuring data quality and performance. Technical requirements include proficiency in Python, Spark, PySpark, SQL, Delta Lake, Unity Catalog, and Databricks Asset Bundles. The role addresses the technical challenge of creating a resilient enterprise observability lakehouse by establishing governance standards, managing metadata, and implementing automated CI/CD pipelines to provide reliable data for security and infrastructure teams.
What does a Data Engineer earn?
Median $162000 from 204 postings across 57 companies.
Design, build, and support scalable Databricks-based data pipelines to ingest and transform observability, infrastructure, and security data.
Develop and optimize PySpark workloads and Structured Streaming jobs using bronze/silver/gold architecture.
Partner with internal engineering teams to onboard new data sources and establish governance standards.
Implement data quality controls and manage metadata to ensure the delivery of trusted enterprise data products.
Contribute to engineering standards, including code reviews, CI/CD practices, and monitoring strategies.
Manage Databricks platform operations including Unity Catalog, security controls, and cost-efficient processing.
Identify and resolve technical debt, process gaps, and operational risks within the observability lakehouse.
What we're looking for
Bachelor’s degree from an accredited university or equivalent work experience (High School Diploma/GED plus 4 years of relevant experience).
5+ years of experience building and supporting enterprise data engineering solutions using Python, Spark, PySpark, SQL, and distributed data processing technologies.
5+ years of experience designing, developing, and operating large-scale data pipelines, data ingestion frameworks, and bronze/silver/gold lakehouse architectures.
4+ years of experience working with Databricks in production environments, including Delta Lake, Unity Catalog, and performance optimization.
3+ years of experience developing high-volume batch and streaming data solutions while implementing data quality controls and improving reliability.
2+ years of experience collaborating with engineering teams, participating in code reviews, and communicating technical solutions to various stakeholders.
Experience building Structured Streaming and near real-time ingestion solutions within Databricks or similar cloud-native platforms (preferred).
Experience implementing Databricks governance capabilities, CI/CD pipelines, or supporting observability and cybersecurity platforms in large-scale environments (preferred).