AI Big Data Engineer

We are seeking a highly skilled and experienced AI Big Data Engineer to design, develop, and optimize large-scale data processing systems. •Position requires 3-days/week onsite in Rockville, MD or Tysons Corner, VA •In this role, you will work closely with cross-functional teams to architect data pipelines, implement data integration solutions, and ensure the performance, scalability, and reliability of big data platforms. The ideal candidate will have deep expertise in distributed systems, cloud platforms, and modern big data technologies such as Hadoop, Spark etc

Responsibilities:

Design, develop, and maintain large-scale data processing pipelines using Big Data technologies (e.g., Hadoop, Spark, Python, Scala). Implement data ingestion, storage, transformation, and analysis of solutions that are scalable, efficient, and reliable. Stay current with industry trends and emerging Big Data technologies to continuously improve the data architecture Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. Optimize and enhance existing data pipelines for performance, scalability, and reliability. Develop automated testing frameworks and implement continuous testing for data quality assurance. Conduct unit, integration, and system testing to ensure the robustness and accuracy of data pipelines. Work with data scientists and analysts to support data-driven decision-making across the organization. Ability to write and maintain automated unit, integration, and end-to-end tests Monitor and troubleshoot data pipelines in production environments to identify and resolve issues.

Education Requirements:

Bachelor's degree in Computer Science, Information Systems or related discipline with at least five (5) years of related experience, or equivalent training and/or work experience Master's degree and past Financial Services industry experience preferred.

Experience Requirements:

Demonstrated technical expertise in Object Oriented and database technologies/concepts which resulted in deployment of enterprise quality solutions. Past experience with developing enterprise quality solutions in an iterative or Agile environment. Extensive knowledge of industry leading software engineering approaches including Test Automation, Build Automation and Configuration Management frameworks. Strong written and verbal technical communication skills. Demonstrated ability to develop effective working relationships that improved the quality of work products. Should be well organized, thorough, and able to handle competing priorities. Ability to maintain focus and develop proficiency in new skills rapidly. Ability to work in a fast-paced environment. Experience with object-oriented programming languages such as Java, Scala or Python.

Essential Technical Skills:

AI Tool Proficiency:

Hands-on experience with AI development tools (GitHub Copilot, Q Developer, ChatGPT, Claude, etc.)

Technical Background:

Strong software development background with ability to contribute to technical discussions

Agile Methodology:

Extensive experience with Scrum, Kanban, and continuous improvement practices

Big Data Technologies :

Experience with Big data technologies such as Hadoop, Spark, Hive & Trino Evaluate understanding of common issues like: Data skew and strategies to mitigate it. Working with massive data volumes in PetaBytes. Troubleshooting job failures due to resource limitations, bad data, scalability challenged.

AI Skills:

Prompt Engineering:

Proficiency in crafting effective prompts for AI coding assistants and analysis tools.

AI Workflow Design:

Experience redesigning development processes to leverage AI capabilities.

Data Analysis:

Ability to interpret AI-generated insights and translate them into actionable team improvements.

Change Management:

Experience leading teams through AI adoption and workflow transformation. SQL Skills (Window Functions, Joins, Complex Queries): SQL window functions, multi-table joins, aggregations. Write/optimize SQL queries on the spot. Apache Spark (Development, Internals & Tuning): Understanding of Sparks core architecture - executors, tasks, stages, DAG.

Spark performance tuning techniques:

partitioning, caching, broadcast joins, etc. Experience optimizing Spark jobs for large-scale datasets. Cloud Technologies -

AWS:

Exposure to AWS services like S3, EMR, Glue, Lambda, Athena, etc. S3 with Spark (e.g., dealing with file formats, consistency issues). EKS, Serverless knowledge, etc. Programming -

Python or Scala :

Ability to write clean, modular, and performant code. Functional programming concepts (e.g., immutability, higher-order functions). Good to

Have:

Experience with managing production data pipelines/ETL systems Experience with CI/CD Experience writing test cases AWS certifications