Note: The job is a remote job and is open to candidates in USA. Guidehouse is seeking a Data Infrastructure Engineer to build and operate the data platform powering its AI/ML analytics modules. The role focuses on scalable data ingestion, ETL/ELT, AWS lakehouse infrastructure, metadata governance, data quality, cloud automation, CI/CD, observability, and cross-team collaboration.

Responsibilities

• Design and implement batch and streaming ingestion from APIs, relational databases, file drops, event streams, and external partners
• Build and optimize ETL/ELT pipelines to produce curated, analytics-ready datasets for reporting and ML consumption
• Implement incremental processing patterns, change data capture (CDC) approaches where appropriate, and data contract standards
• Build and manage a scalable lakehouse on AWS object storage (e.g., S3) using open table/file formats and delta/lakehouse concepts (e.g., ACID tables, schema evolution, time travel patterns)
• Optimize performance and cost through partitioning, compaction, lifecycle policies, and efficient compute/storage usage
• Establish environment standards for dev/test/prod and consistent promotion across stages
• Implement a managed metadata repository for dataset cataloging, ownership, glossary/definitions, tagging, and discoverability
• Enable end-to-end lineage (source → transformations → consumption) to support auditability and impact analysis
• Implement governance controls including policy-based access, data classification, retention, and secure data handling
• Build operational data quality checks (freshness, completeness, validity, anomaly detection) and publish SLAs/SLOs
• Implement automated cloud provisioning in AWS using Infrastructure as Code (IaC) for consistent environments and secure-by-default baselines
• Build and enhance CI/CD for data pipelines, including automated tests, validation gates, promotion workflows, and rollback strategies
• Improve observability with metrics/logs/alerts, dashboards, runbooks, and incident response readiness
• Work closely with engineering, security, networking, and application teams to support mission needs and delivery timelines
• Maintain high-quality engineering documentation including SOPs, system diagrams, and secure configuration baselines
• Summarize and present findings and recommendations—both written and verbal—to technical and non-technical stakeholders

Skills

• 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
• Bachelor's degree in Engineering, IT, Computer Science, or related field (or equivalent experience)
• Minimum of SIX (6) years experience building production data pipelines and/or data platforms
• Strong experience implementing data ingestion and ETL/ELT workflows, including data modeling and transformation best practices
• Hands-on experience building a data lake / delta lake (lakehouse) on AWS (or equivalent cloud) using object storage and modern table formats/patterns
• Proficiency in SQL and one programming language commonly used for data engineering (Python preferred; Scala/Java acceptable)
• Experience with metadata management and governance: cataloging, lineage, ownership, access controls, classification and policy enforcement
• Experience implementing automated AWS provisioning using IaC and operating across multiple environments
• Experience building or operating CI/CD pipelines for data workflows (testing, packaging, deployment automation, environment promotion)
• Solid security fundamentals: IAM/least privilege, encryption, secrets management, secure SDLC practices
• Candidates with an ACTIVE PUBLIC TRUST or SUITABILITY are preferred
• Hands-on experience with Databricks
• Hands-on experience utilizing modern DevOps practices, including tools like Git, Terraform, Jenkins, AWS CodePipeline, and Docker
• Experience utilizing AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT, Cursor, Kiro) to safely accelerate implementation while maintaining strict code quality through testing, code reviews, and security practices
• Knowledge graph and Graph RAG experience, including:
• Graph modeling and ontology/taxonomy alignment
• Entity resolution and relationship extraction
• Hybrid retrieval approaches combining graph traversal with semantic/vector search to improve grounding and explainability

Benefits

• Medical, Rx, Dental & Vision Insurance
• Personal and Family Sick Time & Company Paid Holidays
• Parental Leave
• 401(k) Retirement Plan
• Group Term Life and Travel Assistance
• Voluntary Life and AD&D Insurance
• Health Savings Account, Health Care & Dependent Care Flexible Spending Accounts
• Transit and Parking Commuter Benefits
• Short-Term & Long-Term Disability
• Tuition Reimbursement, Personal Development, Certifications & Learning Opportunities
• Employee Referral Program
• Corporate Sponsored Events & Community Outreach
• Care.com annual membership
• Employee Assistance Program
• Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
• Position may be eligible for a discretionary variable incentive bonus

Company Overview

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