Big Data Engineer — Build Scalable Pipelines & AI Tools

Unisys; Rockville (MD); On-site; USD 120,000 - 170,000; Full time

Job summary

Unisys is seeking a seasoned Big Data Engineer to design, develop, and optimize large-scale data processing systems. You will architect data pipelines, implement data integration, and ensure performance, scalability, and reliability across platforms.

The role emphasizes distributed systems, cloud platforms, and modern big data tech such as Hadoop and Spark. Collaboration with data scientists and analysts is essential, with a focus on robust testing and production monitoring.

Qualifications

• Bachelor's degree in Computer Science, Information Systems or related field with 5+ years of related experience.
• Experience building enterprise-grade data pipelines in an Agile environment.
• Strong knowledge of software engineering with emphasis on test automation and CI/CD.

Responsibilities

• Design, develop, and maintain large-scale data processing pipelines using Big Data technologies.
• Implement data ingestion, storage, transformation, and analysis for scalable systems.
• Collaborate with cross-functional teams to translate business needs into technical solutions.
• Optimize pipelines for performance, scalability, and reliability.
• Develop automated testing frameworks and implement data quality assurance.
• Monitor and troubleshoot data pipelines in production environments.

Skills

Java

Python

Scala

Big data thinking

Data analysis

Problem solving

AI tools awareness

Cloud platforms

Education

Bachelor's degree in Computer Science or related

5+ years of related experience

Master's degree preferred

Tools

Hadoop

Spark

Hive

Trino

AWS S3/EMR/Glue

Job description

Unisys is seeking a seasoned Big Data Engineer to design, develop, and optimize large-scale data processing systems. You will architect data pipelines, implement data integration, and ensure performance, scalability, and reliability across platforms.

The role emphasizes distributed systems, cloud platforms, and modern big data tech such as Hadoop and Spark. Collaboration with data scientists and analysts is essential, with a focus on robust testing and production monitoring.