Senior Data Engineer (Analytics & Applied AI)

Guidehouse is hiring a Senior Data Engineer to build and operate production data platforms that support reliable analytics and applications. The role owns cloud data pipelines and data quality end to end, makes architecture decisions, and contributes to applied AI/ML work.

Responsibilities:

• Own data platforms end to end — from source ingestion through transformation to the analytics and application layers that consume them — with correctness, idempotency, and auditability as first-class requirements
• Write production Python and build software, not just SQL — services, APIs, tooling, and well-tested, maintainable code. This is a software-engineering role as much as a data role
• Design and orchestrate cloud data pipelines on AWS with scheduling, retries, data-quality gates, and clean failure handling
• Model data for analytics and application serving — dimensional/fact modeling, grain decisions, slowly-changing dimensions, idempotent recompute patterns, and the distinction between an analytical warehouse and an application system-of-record
• Implement data-quality and reconciliation checks that run every pipeline and gate publication — you don't ship numbers you can't defend
• Drive architecture and technical decisions independently — evaluate tradeoffs, choose approaches, and lead implementation with limited supervision
• Use agentic coding assistants effectively — leverage AI development tooling to accelerate delivery while maintaining code quality and review discipline
• Grow into applied AI/ML — entity resolution / record linkage, LLM-assisted analytics over structured and unstructured data, and anomaly/risk scoring as the work expands

Requirements:

• SIX (6+) years in data engineering and/or software engineering building and operating production systems
• University Degree and minimum SIX (6) years of prior relevant experience; FOUR (4) years of relevant experience may be substituted for formal education
• Strong software engineering — production-quality Python, clean architecture, testing, code review, and CI/CD. You build software, not just scripts
• Expert SQL — window functions, complex joins, aggregation-grain reasoning, and query performance
• AWS data & compute stack — hands-on with several of: Glue, Step Functions, S3, Redshift, Aurora/RDS, Lambda, EventBridge, ECS/Fargate, IAM
• Data modeling — dimensional/analytical modeling, grain, SCD, and idempotent recompute patterns
• Data-quality discipline — you validate outputs, reconcile totals, and defend your numbers
• Infrastructure as code — Terraform or CDK in a real project
• High autonomy — proven ability to own systems and drive them to completion independently, with sound judgment on architecture and tradeoffs
• Agentic coding assistants — demonstrated, effective use of AI coding tools in a real workflow
• Analytics fluency — profile a dataset, spot anomalies, reason about what a metric means, and explain discrepancies
• Must be able to obtain and maintain a Federal or DoD "public trust"; candidates must obtain approved adjudication of their public trust prior to onboarding with Guidehouse
• Candidates with an active public trust or suitability are preferred
• Applied AI / ML — practical LLM use (RAG, structured extraction from unstructured text), embeddings, or ML risk-scoring; strong interest counts even without deep production experience
• Agent frameworks — AWS Strands Agents, Bedrock AgentCore, or similar for building tool-using / multi-agent systems
• Entity resolution / record linkage — probabilistic matching
• API development — FastAPI or similar; enough front-end awareness to collaborate with UI engineers
• DuckDB / Parquet-based data flows and data-lake table formats (Iceberg/Delta)
• Experience in regulated or government data environments