Databricks Data Engineer

Guidehouse•Tysons, VA

17h•$113,000 - $188,000•Remote

About The Position

Guidehouse is seeking a Databricks Data Engineer for its AI & Data team to support client projects involving large-scale data pipelines, data transformation, platform modernization, and delivery of advanced analytics and AI solutions. The role designs, builds, and maintains scalable data solutions using Databricks, Spark, SQL, Python, Delta Lake, and related cloud-native technologies. It requires hands-on technical expertise, problem-solving, and collaboration with clients and cross-functional teams to deliver reliable, secure, and well-documented data engineering solutions. The position offers virtual work flexibility within the United States. Remote candidates will be considered, with preference for candidates near Guidehouse offices in Arlington, VA; Washington, DC; New York, NY; Chicago, IL; Austin, TX; Atlanta, GA; Boston, MA; or Boulder, CO. The requisition supports hiring across multiple levels. Compensation varies according to the selected candidate’s experience, qualifications, location, and hiring level. The annual salary range is $113,000 to $188,000. Guidehouse offers a comprehensive total rewards package, including competitive compensation and a flexible benefits package intended to support a diverse and supportive workplace.

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 for ingestion, transformation, validation, and publishing 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 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.