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# Enterprise Data Engineer

- **Company:** Hoplite Solutions LLC
- **Location:** Herndon, VA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Airflow, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Databases, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Structures, Relational Databases, File Systems, Apache Hadoop, Python (Programming Language), SQL Databases, Unstructured Data, Enterprise Data Management, Data Processing, Apache Spark, Information Technology, Data Pipelines
- **Published:** October 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a682c07c109d4b6c

## About the Role

Hoplite Solutions is seeking detail-oriented Data Engineers to build and maintain reliable data pipelines using Python, SQL, and ETL platforms such as Palantir. This role will focus on extracting, transforming, curating, and loading structured and unstructured data from diverse sources to support business, analytics, and operational needs., 3+ years of data engineering experience.
Strong proficiency with Python and SQL for data processing, querying, transformation, and optimization.
Experience integrating data from APIs, relational databases, file systems, and other diverse sources.
Experience supporting data quality, validation, cleansing, and monitoring practices.
Strong analytical, problem-solving, communication, and teamwork skills., Bachelor's degree in Computer Science, Data Engineering, or a related field.
Hands-on experience with Palantir or similar ETL platforms.
Experience with cloud platforms such as AWS, Azure, or GCP and big data technologies such as Spark or Hadoop.
Familiarity with data orchestration tools such as Apache Airflow, Prefect, or similar platforms.

## Description

Key Responsibilities: Develop Python and SQL scripts to automate data processing; integrate data from databases, APIs, and file systems; implement data transformation, enrichment, and modeling workflows; optimize data structures for performance and scalability; maintain data quality through validation, cleansing, and monitoring; and provide technical support for effective use of ETL tools across teams.