Databricks Data Engineer

Guidehouse•Tysons, VA

3d•$113,000 - $188,000•Remote

About The Position

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.

Requirements

Bachelor’s degree in computer science, engineering, mathematics, statistics, or another relevant field.
3-8 years of relevant experience in data engineering, data architecture, or cloud data platform implementation.
Strong experience with Python, PySpark, and SQL for data transformation, pipeline development, and data processing.
Experience with Databricks, Spark, Delta Lake, Unity Catalog, or similar cloud-native data platforms.
Experience developing batch or streaming data pipelines, ETL/ELT workflows, and reusable data assets.
Experience with data modeling, data warehousing, data quality validation, and large-scale data processing concepts.
Ability to troubleshoot technical issues, communicate engineering recommendations clearly, and work effectively in team-based delivery environments.
Experience applying data governance, access control, lineage, and security practices within cloud data platforms.

Nice To Haves

2+ years of hands-on experience with the Databricks platform.
Experience using Unity Catalog for data governance, schema design, data modeling standards, access controls, lineage, and secure management of enterprise data assets.
Experience designing and building standard medallion data architectures for scalable ingestion, transformation, quality validation, and curated data delivery.
Active Databricks Data Engineer Associate, Databricks Data Engineer Professional, or related certification.
Experience with CI/CD tools and practices for notebooks, workflows, orchestration jobs, and infrastructure deployment.
Experience with cloud platforms such as Azure, AWS, or GCP.
Experience working in project-based or consulting delivery environments.
Familiarity with streaming frameworks, orchestration tools, Terraform, or related data platform administration capabilities.

Responsibilities

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.
Document pipelines, datasets, data models, workflows, and engineering decisions to support maintainability, transparency, and reuse.
Follow data governance, security, lineage, and compliance standards within the Databricks platform.