> Markdown version of [/jobs/ext/4013326-data-engineer](https://www.wearedevelopers.com/jobs/ext/4013326-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). --- # Data Engineer - **Company:** Pyramid Consulting Inc. - **Location:** Falls Church, VA, United States - **Experience:** Expert - **Salary:** $114,400.0 - $124,800.0 - **Contract:** Temporary contract - **Skills:** Amazon Web Services, Cloud Database, Computer Programming, Information Engineering, Extract Transform Load (ETL), Data Transformation, Distributed Computing Environment, Cloud Services, Unstructured Data, Data Processing, Pyspark, AWS Data Analytics, Data Pipelines - **Published:** October 29, 2026 - **Apply:** https://www.thejobnetwork.com/job/d9e88958-c3cc-4dfa-b11c-c90ba70abb8c/data-engineer ## About the Role * 5+ years of strong hands-on experience with PySpark. * 5+ years of strong programming experience in Scala. * 5+ years of strong experience with AWS cloud services and data engineering. * Experience building and supporting production-grade data pipelines. * Good understanding of distributed data processing and cloud data architecture. ## Description Job DescriptionImmediate need for a talented Data Engineer. This is a 06+ Months Contract opportunity with long-term potential and is located in Mclean, VA (Hybrid). Please review the job description below and contact me ASAP if you are interested., * Develop and maintain scalable data pipelines using PySpark and Scala. * Design and implement data processing solutions on AWS. * Build efficient ETL/ELT pipelines for large-scale structured and unstructured data. * Optimize PySpark/Scala jobs for performance, scalability, and reliability. * Work with AWS data services and cloud-based data architectures. * Perform data transformation, cleansing, validation, and integration. * Troubleshoot data pipeline issues and improve overall system performance. * Collaborate with data architects, analysts, developers, and business stakeholders. * Follow best practices for data quality, security, monitoring, and governance.