Lakehouse Architect / Databricks SME

CACI International•Washington, DC

2d•$114,600 - $252,100•Remote

About The Position

CACI is seeking an expert Lakehouse Architect / Databricks Subject Matter Expert to support the Department of Homeland Security (DHS) Office of the Inspector General (OIG). This role offers a unique opportunity to architect and implement a modern enterprise data platform that transforms how federal oversight professionals access, analyze, and derive insights from investigative and operational data. As a Lakehouse Architect / Databricks SME, you will design and lead the implementation of a secure, scalable data lakehouse using Azure Databricks and Azure Data Lake that serves as the authoritative data foundation for AI, advanced analytics, and investigative workflows. You will architect tiered storage zones, design data catalog integration, establish compute configurations optimized for diverse analytical workloads, and modernize analyst workflows from local desktop processing to collaborative, cloud-based environments. From supporting relational, graph, and geospatial data models to enabling machine learning infrastructure, you will shape the technical architecture that powers insights from investigative records, hotline complaints, and federal datasets. Working within secure Azure Government environments, you will build the data platform foundation for a transformational program of national importance. Join us to make a meaningful impact by architecting the enterprise data infrastructure that enables next-generation federal oversight capabilities.

Requirements

Bachelor's degree + 15 years of experience in data architecture, data engineering, analytics engineering, or related field; equivalencies considered (Master's + 12 years; 21 years with no degree; AA + 17 years)
Must be able to obtain a Active DHS/ EOD Clearance as required.
Deep expertise in Azure Databricks with demonstrated experience architecting and implementing lakehouse solutions using Delta Lake, Unity Catalog, and medallion architecture patterns
Proven experience designing enterprise data platforms supporting diverse analytical workloads including SQL analytics, data science notebooks (Python, R, Scala), machine learning, and visualization integration
Strong background in data modeling across multiple paradigms including dimensional modeling, relational databases, graph databases, and data lake/lakehouse architectures
Extensive hands-on experience with Azure Data Lake Storage, PySpark, SQL, performance optimization, compute configuration, and cost management for cloud analytics platforms

Nice To Haves

Experience implementing Databricks in Azure Government or other secure government cloud environments (AWS GovCloud, secure enclaves) with understanding of FedRAMP, NIST 800-53, and federal security requirements
Hands-on experience integrating Databricks with Azure AI services, MLflow, feature engineering frameworks, and supporting machine learning operations (MLOps) and AI/ML model pipelines
Background in federal government, law enforcement, investigative analytics, or national security data environments with understanding of sensitive data handling, data governance, and analytical workflows for oversight missions

Responsibilities

Lead the design and implementation of a secure, scalable enterprise data lakehouse platform using Azure Databricks, Azure Data Lake Storage, Delta Lake, and Unity Catalog to centralize investigative, operational, and analytical data
Architect lakehouse storage zones supporting tiered data organization (raw/bronze, curated/silver, analytics-ready/gold) with data quality, transformation, and governance controls at each layer
Design and implement environment configurations including workspace architecture, compute cluster strategies, autoscaling policies, job orchestration, and cost optimization approaches for diverse analytical and AI/ML workloads
Design data catalog integration, metadata management, and SQL-accessible tables/views over stored files to enable governed data discovery, lineage tracking, and self-service analytics
Support multiple analytical models and use cases including relational data warehousing, graph/network analysis for investigative linkages, geospatial analytics, and machine learning feature engineering
Lead modernization of analyst and data scientist workflows from local workstation-based processing (Excel, R scripts, desktop tools) to collaborative, cloud-based notebooks, SQL analytics, and platform-native tools
Provide technical expertise and surge support for complex data architecture challenges, analytical database design, performance optimization, and specialized data analysis requirements
Design and implement machine learning engineering infrastructure including MLflow integration, feature stores, model training environments, and integration patterns between Databricks and AI platform services
Develop technical content for data platform training, best practices guidance, workflow documentation, and user enablement materials tailored to investigators, analysts, auditors, and data scientists
Collaborate with Government stakeholders, data engineers, AI engineers, and governance specialists to align lakehouse architecture with mission analytics requirements, data governance policies, and Agile delivery processes.