Big Data/Spark Engineer C2C jobs Location: locations: Rockville MD or Tysons VA

Role : Big Data/Spark Engineer

Location:  locations: Rockville MD or Tysons VA.

Contract

Big Data/Spark Engineer for a contract assignment with one of our premier Regulatory clients. Must be local

to one of these office locations: Rockville MD or Tysons VA.

Hybrid work is required – non local candidates will not be accepted.

2-step interview process – onsite interview mandatory!

Only top 2 candidates will be presented – to build scalable data pipelines.

High-volume AWS environment, extremely large data.

Top Skills: Spark, SQL, Python, AI assisted workflows, Automation validation.

Seeking a Big Data Engineer to design, develop, and optimize large-scale data processing systems. 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. Ideally 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.

Required Skills :

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.

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.

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.

Understanding of common issues like:

Data skew and strategies to mitigate it.

Working with massive data volumes in PetaBytes.

Troublehsooting job failures due to resource limitations, bad data, scalability challenged.

Real-world debugging and mitigation stories.

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)

Comfort with SQL window functions, multi-table joins, aggregations.

Provide examples of write/optimize SQL queries.

Handle edge cases like NULLs, duplicates, ordering, etc.

Apache Spark (Development, Internals & Tuning)

Understanding of Spark’s core architecture — executors, tasks, stages, DAG.

Spark performance tuning techniques: partitioning, caching, broadcast joins, etc.

Troubleshooting slow running/stuck jobs or resource issues in Spark.

Experience optimizing Spark jobs for large-scale datasets.

Cloud Technologies

Exposure to AWS services like S3, EMR, Glue, Lambda, Athena, etc.

Use 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).

Real-world use cases where they wrote scalable data processing code.

Understanding of collections, concurrency, and memory management.

Good to have:

Experience with managing production data pipelines/ETL systems.

Experience with CI/CD.

Experience writing test cases.

AWS certifications.