Databricks Data Engineer

i4DM•Millersville, MD

About The Position

We are seeking a hands-on Databricks Engineer to design, build, and operate scalable data and analytics solutions on the Databricks Lakehouse platform in support of federal mission needs. The ideal candidate will have strong practical experience with Apache Spark, Delta Lake, and Unity Catalog, along with a solid understanding of modern data architecture patterns such as the medallion architecture and structured streaming. This role involves developing and optimizing data pipelines, implementing data governance and security controls, enabling advanced analytics and machine learning, and collaborating with cross-functional teams within a compliance-driven federal environment. By joining our organization, you'll help modernize how federal agencies use data to make better decisions and deliver better outcomes for the people they serve!

Requirements

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience).
4+ years of experience in data engineering, analytics engineering, or big data development.
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 both technical and non-technical stakeholders.

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.