Data Engineer - Big Data & AI/ML Pipelines

MANTECH; Chantilly (VA); On-site; USD 110,000 - 150,000; Full time

Job summary

MANTECH in Chantilly, VA seeks a Data Engineer to design, build, and optimize enterprise-scale data pipelines for analytics and AI workloads. You will transform raw data into trusted, accessible datasets for data scientists and business users alike.

Collaborate across teams to implement robust ETL, maintain data catalogs and documentation, and ensure secure, scalable ingestion, processing, and delivery of datasets throughout the data lifecycle.

Qualifications

• Experience with data analysis, parsing, and programming in Python or Java.
• Significant SQL and database management experience.
• Experience with end-to-end data lifecycle in a Big Data environment.
• Hands-on experience with Java technologies (JDK/J2EE/EJB/JDBC/Spring).

Responsibilities

• Design, implement, and optimize data ingestion, cleansing, transformation, loading, replication/distribution, and large-scale ingest systems in a Big Data environment.
• Optimize all stages of the data lifecycle from initial planning to ingest through final display.
• Develop custom solutions to ingest and exploit new data sources.
• Develop data profiling, deduping, and matching logic for analysis.
• Organize and maintain Data Layer documentation while working with data scientists on AI/ML pipelines.
• Collaborate with teammates, service providers, vendors, and users to improve efficiency.
• Articulate risks and constraints associated with software solutions.

Skills

Python

Java

SQL

Big Data

Data pipelines

ETL

Tools

Apache Airflow

RESTful APIs

Linux

JDK/J2EE/Spring

Job description

MANTECH in Chantilly, VA seeks a Data Engineer to design, build, and optimize enterprise-scale data pipelines for analytics and AI workloads. You will transform raw data into trusted, accessible datasets for data scientists and business users alike.

Collaborate across teams to implement robust ETL, maintain data catalogs and documentation, and ensure secure, scalable ingestion, processing, and delivery of datasets throughout the data lifecycle.