Data Platform Administrator

Peraton•,

18h•$112,000 - $179,000

About The Position

The Data Platform Administrator plays a critical role in managing, optimizing, and governing the core data platforms that power the CMS FPS program's cloud-based, high-performance analytics environment. The Data Platform Administrator will be the primary technical owner of the end users' access and permissions to Snowflake and/or Databricks platforms. This role is responsible for ensuring these systems are performant, secure, well-governed, and accessible to data scientists, analysts, and engineers across the program. The position will oversee platform configuration, user and access management, cost optimization, and integration with upstream and downstream data systems. This requires close collaboration with data scientists, ML engineers, platform infrastructure teams, and program stakeholders to ensure the analytics environment remains stable, compliant, and aligned with evolving program needs.

Requirements

Bachelor's degree with 9+ years of experience in data platform administration or a related technical field.
Hands-on experience administering Snowflake environments, including virtual warehouse management, user/role-based access control, database/schema design, query optimization, and data sharing configurations.
Demonstrated ability to configure and manage Databricks workspaces, clusters, job pipelines, and access policies, including experience with cluster sizing, autoscaling, and cost management.
Strong proficiency in SQL for data querying, platform administration tasks, and performance tuning.
Experience enforcing data governance frameworks, role-based access control, and data security policies.
Familiarity with PII/PHI handling requirements and encrypted data storage standards.
Working knowledge of AWS services (S3, IAM, Lambda) and their integration with Snowflake and Databricks for data ingestion, storage, and pipeline orchestration.
Ability to monitor platform health, analyze query performance, identify bottlenecks, and implement optimizations to improve reliability and reduce cost.
Familiarity with SDLC fundamentals and experience using GitHub for version control and collaborative configuration management.
Excellent written and verbal communication skills, with the ability to support data scientists, engineers, and program stakeholders and clearly communicate platform changes and issues.
Ability to obtain and maintain a Public Trust clearance.
US Citizenship or Green Card holder and must have been in the US at least 3 of the last 5 years.

Nice To Haves

Experience with Databricks Unity Catalog for unified data governance, lineage tracking, and fine-grained access control across workspaces.
Familiarity with Databricks Feature Store for managing and sharing ML features across model development workflows.
Experience with Databricks REST APIs for programmatic workspace management, job orchestration, and platform automation.
Advanced Apache Spark architecture knowledge and experience tuning distributed data processing workloads for performance and cost efficiency.
Experience with Snowflake Data Sharing, external stages, and Snowpipe for automated, continuous data ingestion.
Familiarity with MLflow and how model governance workflows interact with the Databricks and Snowflake platform layers.
Prior experience working within CMS, including familiarity with CMS data systems, workflows, and governance structures.
Knowledge of healthcare claims data, Medicare/Medicaid program data, or federal health analytics use cases.
Experience with Confluence/JIRA for SAFe Agile project tracking and cross-functional documentation.

Responsibilities

Primary technical owner of the end users' access and permissions to Snowflake and/or Databricks platforms.
Ensuring these systems are performant, secure, well-governed, and accessible to data scientists, analysts, and engineers across the program.
Oversee platform configuration, user and access management, cost optimization, and integration with upstream and downstream data systems.
Close collaboration with data scientists, ML engineers, platform infrastructure teams, and program stakeholders to ensure the analytics environment remains stable, compliant, and aligned with evolving program needs.