Databricks Data Engineer

i4DM•Millersville, MD

About The Position

We are seeking a hands-on Databricks Data Engineer to support ATLAS, the VA’s open-architected, multi-cloud enterprise data management and analytics platform. The role designs, builds, and operates scalable lakehouse solutions that ingest, condition, curate, and transform multi-source data into reliable, reusable enterprise data products. Using Databricks, Apache Spark, Delta Lake, Unity Catalog, structured streaming, and medallion architecture patterns, the engineer strengthens interoperability across ATLAS and connected data platforms while ensuring consistent, secure access to governed data. The role collaborates with data scientists, analysts, product teams, and enterprise stakeholders to enable advanced analytics and machine-learning use cases that support evidence-based decisions and improve outcomes for Veterans and their families.

Requirements

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent experience.
At least 4 years of experience in data engineering, analytics engineering, or big data development.
At least 2 years of hands-on experience with the Databricks platform.
Proficiency in Apache Spark, PySpark, and Spark SQL.
Experience with Databricks clusters, jobs/workflows, Delta Lake, and Unity Catalog in production environments.
Experience with medallion architecture and Spark Structured Streaming.
Strong Python and SQL skills for data engineering and data analysis.
Experience with ETL/ELT processes and data pipeline orchestration.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud and their native data services.
Experience integrating data solutions with CI/CD pipelines and Git-based version control workflows.
Understanding of data governance, security, and access control best practices.
Experience working in Agile development environments.
Ability to obtain and maintain a Public Trust determination.
Excellent analytical, problem-solving, and communication skills, with the ability to work with technical and non-technical stakeholders.
Experience with Synapse Pipelines, Azure Data Factory, Starburst, Azure Data Lake Service, Collibra for data cataloging, Azure ML/AI services, and Pyramid Analytics/Power BI for visualization.

Nice To Haves

Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional) or cloud platform certification.
Experience implementing ML or AI solutions in Databricks, including MLflow-based model lifecycle management.
Knowledge of machine learning, AI, or Natural Language Processing (NLP) techniques, including text mining.
Experience supporting fraud analytics, risk scoring, or anomaly detection.
Experience with distributed data and streaming tools such as Kafka, Hadoop, Hive, or Amazon EMR.
Experience with data quality frameworks and observability/monitoring tooling.
Experience with NoSQL databases.
Experience with visualization packages such as Plotly, Seaborn, or ggplot2.
Experience supporting federal government or regulated-industry programs, especially the Department of Veterans Affairs.

Responsibilities

Design, develop, and maintain scalable batch and streaming data pipelines using Databricks, Apache Spark, PySpark, and Spark SQL.
Build and manage Delta Lake tables using the medallion (bronze/silver/gold) architecture to deliver reliable, analytics-ready data.
Develop real-time and near-real-time data ingestion solutions using Spark Structured Streaming and messaging platforms such as Kafka.
Configure and manage Databricks clusters, jobs, and workflows in production environments.
Implement data governance, access controls, and security best practices using Unity Catalog.
Integrate data from a variety of source systems and destinations, supporting ETL/ELT and pipeline orchestration activities.
Optimize existing data workflows and Spark jobs for performance, reliability, and cost efficiency.
Integrate Databricks development with CI/CD pipelines and enterprise SDLC tooling, including Git-based version control.
Collaborate with data scientists and analysts to define data models and support machine learning and AI use cases, including model lifecycle management with MLflow.
Support advanced analytics use cases such as anomaly detection, risk scoring, and fraud analytics.
Monitor and troubleshoot data processing jobs, implementing data quality checks and observability to ensure high availability.
Document data processes, frameworks, pipelines, and data mappings for technical and non-technical audiences.
Work closely with scrum teams, product owners, and client stakeholders to deliver end-to-end data solutions.
Stay current on Databricks platform capabilities and industry trends to recommend best-fit tools and technologies.