Title: Big Data Engineer

Location: Rockville, MD or McLean, VA (Hybrid)

Contract: 6+ Months Contract

during the test if candidiate cheat or copy paste any thing then all these thing will be monitorised and then profile will be rejected .

so no fake submission please .

please give me best excellent genuine candidiate sure shot 100% interview and offer for right candidate 

give me local candidiate only no relocation no represent 

 interview: video+f2f

Only Local candidates who can take Assessment and only who are in DC/VA/MD who can got for F2F interview

Overview

• Must haves: spark, Hadoop, scala, hive
• scripting is a must- python or perl
• must be expert level in Complex SQL- window functioning, complex multiple joins, cloud experience is mandatory-S3, glue, emr, athena
• AI- How to use AI for prompt engineering
• Github
• Copiliot
• Chjatgopt
• Q

Need someone who is well versed in agile, test automations, CICD practices

Financial experience is preferred

ROLE FIT

• 5+ years building enterprise-scale data solutions using Spark, Hadoop, Hive, and Scala
• Strong scripting skills (Python or Perl) and expert-level complex SQL (window functions, multi-joins)
• AWS cloud experience required (S3, EMR, Glue, Athena)
• Experience with Agile delivery, CI/CD pipelines, automated testing, and GitHub workflows
• Financial services or regulated industry experience preferred
• Design and maintain scalable, reliable big data pipelines
• Optimize Spark/Hadoop workloads for performance, scalability, and cost efficiency
• Implement automated testing and data quality validation
• Enable analytics and data science teams with high-quality, accessible datasets
• Leverage AI-assisted tools (Copilot, ChatGPT, Q Developer) to improve development productivity
• Diagnose and resolve Spark performance bottlenecks and data pipeline failures
• Optimize complex SQL transformations and large-scale joins
• Troubleshoot data quality, latency, and reliability issues in production
• Improve AWS workload efficiency through tuning and resource optimization
• Automate repetitive engineering tasks using AI-assisted development tools
• Delivered end-to-end pipelines using Spark and Hadoop ecosystem tools
• Optimized SQL and pipeline performance with measurable improvements
• Deployed and supported AWS data workloads (EMR, Glue, Athena, S3)
• Implemented CI/CD and automated testing for data pipelines
• Used AI coding assistants and GitHub workflows in team-based development