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# Data Engineer

- **Company:** Absolute Business Solutions Corp
- **Location:** Springfield, VA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $120,000.0 - $150,000.0
- **Contract:** Permanent contract
- **Skills:** Code Reuse, Software Debugging, Distributed Data Store, Python (Programming Language), SQL Databases, Cloud Platform System, Apache Spark, Jupyter, Containerization, Kubernetes, Data Pipelines, Databricks
- **Published:** June 16, 2026
- **Apply:** https://www.clearancejobs.com/jobs/8980229/data-engineer

## About the Role

* Bachelor's Degree with at least five years' relevant experience. Will consider additional years' experience in lieu of degree.
 * Strong Python experience
 * Experience with automation of job monitoring, optimization, and debugging at scale
 * Experience working with any of the major cloud providers
 * Excellent communication skills with the ability to work in cross-functional teams

Desired Skills

 * Apache Spark
 * Background in building and maintaining CI/CD pipelines
 * Knowledge of Kubernetes and containerization
 * Experience building dashboards
 * Using notebook-based tools such as Jupyter and Databricks
 * Knowledge of Scala, SQL and R

## Description

We are actively hiring a TS/SCI-cleared Data Engineer to work closely with our sister company/software Syntasa to design scalable data pipelines, optimize Spark workloads, and deliver high-performance cloud solutions in support of our IC customer's Data Modernization Services. You'll be working across all major cloud providers to build cost-efficient, production-ready systems that power advanced analytics and AI initiatives. This role is a hybrid role (on-site Syntasa, customer site, and telework) in Herndon, VA; St. Louis, MO; or Aurora, CO., * Optimize large-scale data pipelines for ingestion, transformation, and processing.

 * Develop robust, reusable code in Python and Spark to support distributed data workflows.
 * Manage and tune Spark jobs on cloud-based platforms with Kubernetes orchestration.
 * Implement scalable data solutions for storage and retrieval.
 * Drive reliability, performance, and cost efficiency across cloud infrastructure.


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