Data Infrastructure Engineer

Job Family:

Software Development & Support

Travel Required:

None

Clearance Required:

Ability to Obtain Public Trust

We are seeking a Data Infrastructure Engineer to build and operate the data platform that powers AI/ML analytics modules. You will design and implement scalable data ingestion pipelines, robust ETL/ELT, and a modern data lake / delta lake (lakehouse) on AWS. You’ll also establish a managed metadata repository and governance layers (catalog, lineage, quality, access controls) and deliver automated cloud provisioning plus CI/CD for data pipelines to enable reliable, repeatable deployments across environments. 

This role is ideal for an engineer who enjoys platform building, automation, and enabling advanced analytics through trusted, well-governed data. 

What You Will Do:

Build & Operate Data Pipelines (Batch + Streaming) 

• 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. 

Deliver a Modern Lakehouse (Data Lake / Delta Lake) 

• 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. 

Metadata, Governance, Lineage & Quality (Trust Layer) 

• 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. 

AWS Automation + CI/CD for Data Pipelines 

• 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. 

Cross-Team Collaboration & Documentation 

• 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. 

What You Will Need:

• 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.

• Bachelor’s degree in Engineering, IT, Computer Science, or related field (or equivalent experience). 

• Minimum of FOUR (4) 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 auto