Data Engineer

KDA CONSULTING INC; Chantilly (VA); On-site; USD 100,000 - 130,000; Full time

Job summary

A technology consulting firm in Chantilly, Virginia, is seeking a highly skilled Data Engineer to design and maintain scalable data pipelines. This role involves leveraging AWS services and implementing best practices for data quality and governance while ensuring compliance with security standards. The ideal candidate has a Bachelor's degree in a technical field, experience with AWS data services, and proficiency in SQL and Python. An active TS/SCI clearance is required.

Qualifications

• Active TS/SCI W/ Polygraph Required.
• Experience in data engineering and pipeline development.
• Proficiency in SQL and Python for data processing and transformation.
• Experience with large-scale data sets and big data technologies.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines.
• Build and optimize data workflows for batch and real-time processing.
• Leverage AWS services to support data engineering solutions.
• Ensure data accuracy, consistency, and integrity across pipelines.
• Collaborate with data scientists and engineers.

Skills

AWS cloud services

Data engineering best practices

Big data technologies

SQL

Python

Analytical skills

Problem-solving

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

AWS services (S3, Glue, Redshift, Lambda, EMR)

Job description

KDA Consulting Inc. is seeking a highly skilled Data Engineer to design, build, and maintain scalable data pipelines and architectures supporting mission-critical programs within the Intelligence Community (IC). This role focuses on enabling efficient data processing, analytics, and decision-making by delivering reliable, high-quality data solutions in cloud environments.

The ideal candidate will have strong experience with AWS cloud services, data engineering best practices, and big data technologies, with the ability to operate in a fast-paced, collaborative environment supporting complex mission systems.

Data Pipeline Development & Engineering

• Design, develop, and maintain scalable ETL/ELT pipelines to ingest, transform, and load data from multiple sources
• Build and optimize data workflows supporting both batch and real-time processing
• Ensure high availability and reliability of data pipelines across enterprise systems

AWS Cloud & Data Services

• Leverage AWS services such as S3, Glue, Lambda, Redshift, EMR, and Kinesis to support data engineering solutions
• Design and manage cloud-based data architectures that are secure, scalable, and cost-efficient
• Implement best practices for data storage, partitioning, and lifecycle management

Data Optimization & Performance

• Optimize data storage and retrieval for performance, scalability, and cost efficiency
• Implement indexing, partitioning, and query optimization strategies
• Monitor and troubleshoot data pipeline performance issues

Data Quality & Governance

• Ensure data accuracy, consistency, and integrity across all data pipelines and systems
• Implement validation, monitoring, and error-handling mechanisms
• Support data governance practices and compliance with security and IC standards

Collaboration & Integration

• Work closely with data scientists, software engineers, and mission stakeholders to support analytics and operational needs
• Integrate data solutions with enterprise applications, APIs, and downstream analytics platforms
• Participate in Agile development processes and contribute to continuous improvement efforts

Requirements

Active TS/ SCI W/ Polygraph Required.

Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent experience).

Demonstrated experience in data engineering and pipeline development.

Strong experience with AWS data services (e.g., S3, Glue, Redshift, Lambda, EMR).

Proficiency in SQL and Python for data processing and transformation.

Experience working with large-scale data sets and big data technologies.

Strong analytical and problem-solving skills.