> Markdown version of [/jobs/ext/3714631-big-data-engineer](https://www.wearedevelopers.com/jobs/ext/3714631-big-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Big Data Engineer - **Company:** Experis - **Location:** McLean, VA, United States - **Salary:** $149,760.0 - $156,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Automation of Tests, Big Data - **Published:** October 13, 2026 - **Apply:** https://www.experis.com/en/job/413120/big-data-engineer-no-c2c- ## About the Role + Bachelor's degree in Computer Science, Information Systems, or related discipline with at least five (5) years of related experience, or equivalent training and/or work experience. + Demonstrated technical expertise in object-oriented and database technologies/concepts, with experience delivering enterprise-quality solutions. + Strong software engineering background with test automation, build automation, and configuration management practices, including iterative or Agile delivery experience. + Hands-on experience with Big Data technologies such as Hadoop, Spark, Hive, and Trino, including debugging and mitigation for issues like data skew and resource-related job failures. + Strong SQL skills (window functions, joins, complex queries) and the ability to write/optimize SQL queries while handling edge cases such as NULLs, duplicates, and ordering. ## Description + Design, develop, and maintain large-scale data processing pipelines using Big Data technologies such as Hadoop, Spark, Python, and Scala. + Implement data ingestion, storage, transformation, and analysis solutions that are scalable, efficient, and reliable. + Optimize and enhance existing data pipelines for performance, scalability, and reliability, including troubleshooting production issues. + Develop automated testing frameworks and implement continuous testing to support data quality assurance. + Collaborate with cross-functional teams, including data scientists and analysts, to support data-driven decision-making. What's Needed?