Databricks Data Engineer

Job Family:

Data Science & Analysis, Data Science Consulting

Travel Required:

None

Clearance Required:

None

Guidehouse is seeking a Databricks Data Engineer to join our AI & Data team to support client projects involving large-scale data pipelines, data transformation, platform modernization, and advanced analytics and AI solution delivery. This role focuses on designing, building, and maintaining scalable data solutions using Databricks, Spark, SQL, Python, Delta Lake, and related cloud-native technologies. The role requires hands-on technical expertise, strong problem-solving skills, and the ability to collaborate with clients and cross-functional teams to deliver reliable, secure, and well-documented data engineering solutions.

This position offers virtual work flexibility within the United States. While remote candidates will be considered, preference will be given to candidates located near a Guidehouse office in one of the following markets: Arlington, VA; Washington, DC; New York, NY; Chicago, IL; Austin, TX; Atlanta, GA; Boston, MA; and Boulder, CO.

Please note, this requisition supports hiring across multiple levels to support our Databricks Data Engineer Teams. The posted salary range represents a range of potential compensation and will vary based on the selected candidate’s experience, qualifications, location, and the level at which the position is filled.

What You Will Do:

• Design, build, and maintain scalable data pipelines using Databricks, PySpark, SQL, Delta Lake, and related cloud-native data engineering tools.

• Develop and support batch and streaming data workflows, including ingestion, transformation, validation, and publishing of curated data products.

• Write, optimize, and maintain Python, PySpark, and SQL code for data processing, orchestration, and performance tuning.

• Work with large-scale datasets using Databricks, Spark, Delta Lake, Unity Catalog, and cloud storage services.

• Troubleshoot and resolve data pipeline failures, performance issues, data quality issues, and workflow bottlenecks.

• Translate business and technical requirements into data engineering designs, processing scripts, and job orchestration workflows.

• Perform data validation, quality checks, code reviews, and issue resolution to support accurate and reliable data products.

• Collaborate with cross-functional teams including solution architects, data scientists, analysts, DevOps engineers, and client stakeholders.

• Communicate technical concepts, delivery impacts, and data engineering considerations to technical and non-technical audiences.