ETL Data Engineer

Description:
Hybrid 3 days onsite / 2 days remote in Mclean, VA

Our client seeks an ETL Data Engineer to build and maintain large-scale data pipelines and to design agentic AI systems that support regulatory analytics. The role spans Spark-based ETL on AWS data lake platforms and development of LLM-powered agents with secure, auditable outputs. The engineer will collaborate across teams, uphold secure development practices, and contribute to CI/CD and infrastructure-as-code while monitoring data quality and production performance.

We can facilitate w2 and corp-to-corp consultants. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.

Rate: $70.00 to $88.00/hr. w2

Responsibilities:

• Build and maintain ETL/ELT pipelines using Apache Spark, Hive, and Trino across S3-based data lakes.
• Develop and optimize SQL for large-scale surveillance datasets using window functions, joins, and complex aggregations.
• Engineer big data systems on EMR-on-EC2 and EMR-on-EKS and deliver solutions on analytical platforms such as Sage Maker, Domino, or Dataiku.
• Participate in data quality monitoring, anomaly detection, and production incident investigation.
• Develop AI agent systems using AWS Bedrock and agent frameworks such as Strands Agents SDK or Lang Chain/Lang Graph.
• Design agent harnesses that combine LLM reasoning with deterministic execution including skill or RAG-based SQL generation and structured output validation.
• Implement agent memory, context management, and tool integration including MCP servers, API connectors, and data catalog lookups.
• Build evaluation frameworks for agent accuracy covering paraphrase robustness, routing precision, and structural consistency.
• Stay informed on advances in LLM frameworks and emerging AI capabilities.
• Write clean, well-tested code and contribute to CI/CD pipelines and infrastructure-as-code on AWS.
• Ensure secure handling of sensitive regulatory data with auditable execution traces.
• Adhere to secure development practices and technology policies.
• Partner across teams, communicate at the appropriate technical level, and maintain documentation on Confluence or Wiki.
• Learn from senior team members and contribute to process improvement.

Experience Requirements:

• Experience building data pipelines with Apache Spark (PySpark preferred) and SQL.
• Experience with SQL engines such as Hive or Trino and cloud data platforms including AWS S3, EMR, and Lambda.
• Understanding of data skew, large-volume processing, and troubleshooting job failures due to resources, data quality, and scalability.
• Hands-on debugging and mitigation experience.
• Practical experience building LLM-powered agent systems that use tools and produce structured outputs.
• Experience with agent frameworks such as Lang Chain, Lang Graph, or AWS Strands.
• Knowledge of prompt engineering, RAG architectures, and context or memory management.
• Experience with foundation model APIs such as Anthropic Claude, Amazon Nova, or OpenAI.
• Understanding of agent memory tiers and strategies for persistence, pruning, and retrieval.
• Familiarity with harness patterns including deterministic guardrails, tool routing, and verification loops.
• Hands-on experience with AI development tools such as Git Hub Copilot, Q Developer, ChatGPT, or Claude.
• Experience with spec-driven development for AI-assisted code generation and validation.
• Ability to leverage AI pair programming for suggestions, debugging, refactoring, and automated test generation.
• Experience with AWS services including S3, EMR, EMR on EKS, Lambda, Bedrock, and Step Functions.
• Hands-on experience using S3 with Spark and related file format or consistency considerations.
• Familiarity with AWS Bedrock guardrails, knowledge bases, and agent orchestration.
• Exposure to Google Cloud Vertex AI or equivalent managed AI platforms.
• Familiarity with AWS monitoring and logging tools such as Cloud Watch and Cloud Trail.
• Proficiency in Python with clean, modular, and performant code and understanding of functional concepts.
• Strong understanding of collections, concurrency, and memory management.
• Proficiency with SQL window functions, joins, aggregations, and complex query optimization including edge cases.

Education Requirements:

• Bachelor's degree in Computer Science, Data Science, Information Systems, or related discipline with at least two years of related experience, or equivalent training and work experience. Financial services experience preferred.
• Demonstrated expertise in object-oriented and database technologies resulting in enterprise-quality solutions.
• Knowledge of software engineering approaches including test automation, build automation, and configuration management.
• Strong written and verbal technical communication skills and effective cross-team collaboration.
• Ability to learn new skills rapidly and operate in a fast-paced environment.

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