Enterprise Data Engineer

Accenture Federal Services•Herndon, VA

8d•$111,800 - $221,800

About The Position

At Accenture Federal Services, our purpose is to make the US federal government stronger and safer, and to improve the lives of citizens through technology and ingenuity. We are seeking skilled and detail-oriented Data Engineers with expertise in utilizing Python, SQL, and Palantir (or other ETL platforms) to extract, transform, and load structured and unstructured data from various sources. The ideal candidate will have a strong background in data engineering and a deep understanding of ETL processes. Join us to drive positive, lasting change that moves missions and the government forward!

Requirements

3+ years of experience in data engineering.
Strong proficiency in scripting and programming with Python.
Expertise in working with SQL for data querying, transformation, and optimization.
Experience with data integration from diverse sources such as APIs, relational databases, and file systems.
Active TS/SCI with Poly clearance is required.

Nice To Haves

Bachelor’s degree in Computer Science, Data Engineering, or a related field.
Hands-on experience with Palantir.
Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Apache Spark, Hadoop).
Familiarity with data orchestration tools such as Apache Airflow, Prefect, or similar.
Strong analytical and problem-solving skills with attention to detail.
Excellent communication and teamwork skills to collaborate with technical and business teams.

Responsibilities

Write robust and efficient Python and SQL scripts to automate data processing tasks.
Integrate data from diverse sources, such as databases, APIs, and flat files, into centralized systems.
Implement data transformations, enrichment, and modeling workflows to ensure data quality and usability.
Ensure data structures are optimized for performance and scalability.
Provide technical guidance and support to ensure effective use of Palantir tools across teams.
Implement data quality checks, validation, and monitoring mechanisms to ensure the accuracy and reliability of datasets.
Integrate data from diverse sources, such as databases, APIs, and flat files, into centralized systems.
Ensure proper data transformation and curation to meet business and analytics requirements.
Maintain and improve data quality, consistency, and accuracy through validation and cleansing processes.