Python Developer

Constellation Inc•Washington, DC

3d•$110,000 - $125,000•Hybrid

About The Position

The Python Developer designs, develops, and implements Python-based automation for reconciliation processes. The role builds robust, scalable ETL data pipelines to extract, transform, and load information from multiple sources into an integrated platform, and develops, tests, and deploys Python automation scripts and workflows to ensure accuracy, efficiency, and reliability throughout the reconciliation lifecycle. Working in cloud and enterprise data environments, the developer collaborates with technical leads and stakeholders to translate requirements into maintainable solutions and continually improve automation performance.

Requirements

At least 3 years of experience in data and automation engineering, with a strong focus on Python.
At least 3 years of experience developing, maintaining, and administering SQL databases.
At least 2 years of experience with cloud-based data platforms such as Databricks, Apache Airflow, or AWS services such as Lambda and S3.
Experience designing, testing, and deploying robust ETL workflows and integrating them with enterprise data systems.
U.S. citizenship and ability to pass a background investigation by the client agency.

Nice To Haves

DHS clearance with CBP/ICE or DoD Top-Secret clearance.
Experience with Optical Character Recognition (OCR) and Document Understanding frameworks, such as AWS Textract, Tesseract, or layout-aware models.
Familiarity with DevOps practices such as version control, CI/CD, and containerized deployment.

Responsibilities

Design, develop, and maintain robust ETL pipelines for integrating data across multiple sources.
Refactor existing code and automated processes into Python, validating functionality through detailed test cases and user feedback.
Implement scalable data solutions using Python and cloud-based platforms such as Databricks and Apache Airflow.
Collaborate with data scientists and analysts to ensure data is accessible and usable.
Optimize data architecture and workflows to improve efficiency, performance, and security.
Troubleshoot data pipeline issues and ensure data integrity across systems.