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# Databricks Engineer :: Washington, DC (Remote)

- **Company:** ARK Solutions, Inc.
- **Location:** Washington, DC, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Business Analytics Applications, Big Data, Computer Programming, Databases, Extract Transform Load (ETL), Data Transformation, Python (Programming Language), Systems Development Life Cycle, Query Optimization, SQL Databases, Data Processing, Scripting, Apache Spark, Pyspark, Data Management, Data Pipelines, Databricks
- **Published:** October 26, 2026
- **Apply:** https://www.careerbuilder.com/job-details/databricks-engineer-washington-dc-remote-washington-dc--f95b75d2-264d-4fad-87e6-2319a9f49bb8

## About the Role

Data Management, Database Extract Transform and Load (ETL), Python Programming/Scripting Language, SQL (Structured Query Language), Data Processing, Scalable System Development, Data Quality, Identify Issues, Problem Solving Skills, Computer Programming, * Hands-on experience with Databricks.

 * Strong programming skills in Python and PySpark.
 * Strong SQL development and query optimization experience.
 * Experience designing, developing, and maintaining ETL pipelines.
 * Experience working with large-scale data processing and analytics solutions.
 * Strong analytical, troubleshooting, and problem-solving skills.
 * Excellent communication and collaboration abilities., * Databricks
 * Python
 * PySpark
 * SQL

Education:

 * Bachelor's Degree

## Description

Seeking for a Databricks Engineer responsible for designing, developing, maintaining, and optimizing scalable data pipelines and ETL processes using Databricks, Python, PySpark, and SQL. This role will play a key part in modernizing data platforms and supporting enterprise data initiatives.

Day-to-day Responsibilities:

 * Create and maintain ETL pipelines and/or migrate existing legacy pipelines (e.g., synapse) to Databricks
 * Develop scalable data processing solutions using Python, PySpark, and SQL.
 * Perform data transformation, cleansing, and validation to ensure data quality and consistency.
 * Optimize data workflows and Spark jobs for performance and efficiency.
 * Collaborate with business stakeholders, data architects, and development teams to understand requirements and deliver effective solutions.
 * Monitor, troubleshoot, and resolve data pipeline issues.