This role pays less than
55%
of similar roles. Most pay
$151,987–$228,725
— the shaded band above.
At the midpoint, this role pays about
$178k
versus about
$190k
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.
Staff Data Engineer, Observability Data Lake joins the Enterprise Observability Platform organization to advance next-generation observability, infrastructure, and security data capabilities. This role involves designing, building, and operating scalable Databricks-based data pipelines and lakehouse solutions that ingest, transform, and govern high-volume telemetry, infrastructure, application, and security data from multiple enterprise sources. The engineer will develop PySpark workloads, Structured Streaming jobs, and bronze/silver/gold architectures while establishing data quality standards and governance controls. Key technical requirements include proficiency in Python, Spark, PySpark, SQL, Delta Lake, Unity Catalog, and Databricks Asset Bundles. The role focuses on solving complex problems related to operational intelligence and security analytics by building resilient ingestion frameworks and optimizing performance for the enterprise observability lakehouse. Collaboration with Security, Infrastructure, and Platform teams ensures high-quality data products and robust metadata management across the organization.
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 cross-functional teams to onboard new data sources and establish governance standards for enterprise data products.
Implement data quality controls, metadata management, and security protocols within the Databricks lakehouse environment.
Drive engineering excellence by contributing to code reviews, CI/CD practices, and monitoring strategies.
Proactively identify and resolve technical debt, process gaps, and operational risks to improve platform reliability.
Manage cost-efficient data platform operations using Unity Catalog and other governance tools.
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).
7+ years of experience building and supporting enterprise data engineering solutions using Python, Spark, PySpark, SQL, and distributed data processing technologies.
6+ years of experience designing, developing, and operating large-scale data pipelines, data ingestion frameworks, and bronze/silver/gold lakehouse architectures.
5+ years of experience working with Databricks in production environments, including Delta Lake, Unity Catalog, and performance optimization.
4+ 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 stakeholders.
Experience building Structured Streaming and near real-time ingestion solutions within Databricks or similar cloud-native platforms (preferred).
Familiarity with OCSF or similar observability, cybersecurity, or security event normalization frameworks (preferred).
Databricks
Delta Lake
Unity Catalog
AWS
Python
Java
SQL
NoSQL
Data Engineering
Data Modeling
Machine Learning
Data Governance
Fivetran
HVR
Vault
Data Observability