Mid-Level Data Engineer, Databricks

JobgetherData engineering

Fully remote position within the United States.Full-TimeMiddle

SalaryAnnual compensation range of $100,000–$140,000 USD.

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Job Details

Experience: At least 5 years of experience in data engineering, data analysis, or a related technical field.
Required Skills: PythonSQLData modelingDatabricksPySpark

Bachelor's degree in computer science, data science, information systems, engineering, or a related discipline, or equivalent professional experience.
At least 5 years of experience in data engineering, data analysis, or a related technical field.
Hands-on experience building and supporting Databricks data pipelines, including notebooks, PySpark or Spark SQL, and Delta tables.
Proficiency in Python and SQL for data transformation, querying, validation, and troubleshooting.
Understanding of ETL/ELT processes, data modeling, data integration, and data quality practices.
Ability to investigate data discrepancies, identify root causes, and implement solutions.
Strong documentation, communication, and problem-solving skills, including explaining technical issues and collaborating with technical and non-technical stakeholders.
Ability to take ownership of assigned work, manage time, adapt to shifting priorities, and keep stakeholders informed.
Experience integrating data through APIs is a nice-to-have.
Familiarity with Databricks Unity Catalog, Databricks Jobs or Workflows, and CI/CD deployment practices is a nice-to-have.
Experience with Google Cloud Platform (GCP) or federal government clients or projects is a nice-to-have.

Design, develop, test, and maintain Databricks pipelines using Python, PySpark, and SQL.
Ingest, transform, and organize data from source systems into layered, reusable tables.
Build and maintain data products for analytics, reporting, and automation.
Validate datasets against source systems, investigate discrepancies, and correct data quality issues.
Apply ETL/ELT practices, data modeling principles, and quality controls.
Document pipelines, table definitions, transformation logic, and business rules.
Own assigned engineering tasks from development through delivery and production support.
Collaborate with engineers, data scientists, government stakeholders, and other team members to clarify requirements and deliver solutions.
Contribute to improvements in data engineering workflows, pipeline reliability, and shared dataset usability.

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