Databricks Administrator

Acuity INC

About The Position

Acuity, Inc. seeks a Databricks Administrator to support a federal client by managing secure, stable, and efficient Databricks environments for data engineering, analytics, and advanced analytics workloads. This role owns administration of Databricks workspaces, compute policies, SQL warehouses, user access, platform configuration, monitoring, and operational support across development, test, and production environments. The Databricks Administrator partners with data engineers, analysts, platform engineers, and security teams to maintain platform health, enforce governance, optimize performance, and support controlled delivery of analytics solutions. Why Acuity? We’re building more than careers—we’re building a place people genuinely love to work. At Acuity, you’ll get: • Strong work-life balance and a people-first culture• Competitive compensation + Health/Dental/Vision coverage• 401(k) with company match• Up to $6,000/year for training & professional development 🏆 Recognized as a Best Places to Work (Washington Business Journal, 8+ years)🏆 Named a Top Workplace by The Washington Post (2022–2025) Ready to grow with a team that invests in you? Learn more: www.myacuity.com Why Acuity? Are you ready to use your expertise in the areas of IT Modernization, Data Enablement, and Hyperautomation to make a real difference? Join Acuity, Inc., a technology consulting firm that supports federal agencies. We combine industry partnerships and long-term federal experience with innovative technical leadership to support our customers’ critical missions.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent relevant experience
5+ years of experience developing big data or data engineering solutions in an Agile environment
Hands-on experience with Databricks, including Delta Lake or lakehouse-based data engineering
Experience developing and deploying ETL/ELT pipelines using Apache Spark
Strong SQL skills, including complex queries, joins, aggregations, and window functions
Experience with AWS and cloud-based data platform services
Experience with Python, PySpark, SQL, or similar data engineering languages and frameworks
Experience with code versioning, CI/CD, and configuration management concepts
Experience working in Agile delivery environments, including Scrum
Ability to troubleshoot and optimize data pipelines for quality, performance, and reliability
Must be a U.S. Citizen with the ability to obtain and maintain U.S. suitability

Nice To Haves

Experience with Delta Lake design and optimization
Experience integrating Databricks with Git-based workflows in controlled network environments
Experience with data quality, lineage, logging, and metadata practices
Experience supporting advanced analytics or machine learning data pipelines
Experience tuning performance in distributed data processing environments

Responsibilities

Design, build, and maintain scalable data pipelines, ETL/ELT processes, and integration workflows
Develop transformations and curated datasets using Databricks, Apache Spark, and SQL
Support ingestion, processing, and delivery of structured, semi-structured, and unstructured data
Build and optimize workflows across data lake, data warehouse, and analytics platforms
Implement data quality checks, validation rules, and operational monitoring across pipelines and datasets
Improve performance, reliability, scalability, and security of data processing solutions
Support cloud-based data engineering solutions in AWS, including storage and data service integration
Collaborate with engineers, architects, analysts, and stakeholders to implement technical requirements
Support Big Data, Advanced Analytics, and Machine Learning use cases from a data engineering perspective
Maintain documentation for pipelines, transformations, data flows, and operational procedures
Support code versioning, CI/CD, and controlled deployment of data engineering assets
Troubleshoot pipeline, data, and platform issues across development, test, and production environments