Big Data Engineer

Job Description - Submit candidates with their DL, Visa Copy, LinkedIn.

Big Data Engineer Location:

Rockville, MD or McLean, VA (Hybrid - 3 days onsite with 2 days remote)

Duration:

6+ months /

EAD MOI:

Prescreen, Phone, Onsite panel Candidate should be local and from nearby state with DL There will be a technical assessment and ID verification before submitting profile to client. Assessment should be completed within 24 hours.

Job Summary:

We are seeking an experienced Big Data Engineer to design, build, and optimize large-scale data processing systems. This role partners closely with engineering, data, and analytics teams to deliver scalable, reliable data pipelines that support data-driven decision-making. The ideal candidate has strong experience with distributed systems, cloud platforms, and modern big data technologies. Qualifications Bachelor''s degree in Computer Science or a related field, or equivalent experience. 5 years of experience building enterprise-scale data solutions. Strong experience with big data technologies such as Spark, Hadoop, Hive, and Trino. Proficiency in Python or Scala, with solid object-oriented and/or functional programming skills. Strong SQL skills, including complex queries, joins, and window functions. Experience working with large datasets and troubleshooting performance, scalability, and data quality issues. Experience with cloud platforms, preferably AWS (e.g., S3, EMR, Glue, Athena, Lambda). Familiarity with Agile development practices and CI/CD pipelines. Strong communication skills and ability to work effectively in a fast-paced environment. Experience managing production ETL or data pipeline systems. Exposure to AI-assisted development tools (e.g., Copilot, ChatGPT, Q Developer). Experience with Spark performance tuning and optimization. AWS certifications or equivalent cloud experience. Responsibilities Design, develop, and maintain scalable data pipelines using technologies such as Spark, Hadoop, Python, and Scala. Build and optimize data ingestion, transformation, and storage solutions for large-scale datasets. Optimize existing data pipelines for performance, scalability, and reliability. Collaborate with cross-functional teams to translate business requirements into technical solutions. Monitor, troubleshoot, and resolve data pipeline issues in production environments. Implement automated testing and data quality checks across pipelines. Stay current with emerging big data and cloud technologies to continuously improve the platform. Support data scientists and analysts by enabling reliable access to high-quality data.