• Design, build, and maintain scalable, reliable, and automated data pipelines using SQL, Python, and Databricks to support enterprise analytics. • Architect and optimize robust data models and infrastructure to ensure high data quality, integrity, and accessibility across the client's ecosystem. • Partner closely with the Data Science team to operationalize their work, deploying statistical and machine learning models into production environments using DataOps best practices. • Identify, design, and implement internal process improvements, including automating manual data processes and optimizing data delivery for scalability. • Collaborate with cross-functional teams to identify business problems, gather requirements, identify data sources, and provide data-driven solutions.

• Design, build, and maintain scalable data pipelines and ETL processes using Microsoft Fabric (Notebooks, Pipelines, Dataflows) to support analytics, reporting, and product use cases. • Integrate data from multiple internal and external sources, ensuring quality, consistency, and reliability across the medallion architecture. • Develop and maintain data models and transformations using dbt, contributing to bronze, silver, and gold layer modeling in the Fabric lakehouse. • Collaborate with engineers, analysts, and product teams to translate business requirements into technical data solutions — communicating through Slack and tracking work in Azure DevOps (ADO). • Participate in data quality checks, testing, validation, and performance optimization across pipeline and model layers. • Monitor, optimize, and troubleshoot data infrastructure for performance and scalability in a cloud-native Azure environment. • Follow engineering best practices around version control and CI/CD using GitHub, including branch management, pull requests, and code review. • Contribute to data documentation and ensure best practices around data governance, reliability, and scalability. • Contribute to the continuous improvement of data engineering processes and tools.

• Design and build medallion architecture in dbt and Microsoft Fabric to enhance data analytics capabilities. • Create and maintain data ingestion pipelines to ensure efficient data flow and integration. • Establish data governance protocols, ensuring compliance with industry standards and best practices. • Implement data lineage and documentation to facilitate data traceability and transparency. • Develop and enforce data quality checks to maintain the integrity and accuracy of data assets. • Collaborate with cross-functional teams to understand data requirements and deliver solutions that meet business needs.

Role Description GovCIO is hiring a Databricks SME to support the Department of Veterans Affairs (VA) Data Modernization initiative. In this role, the Databricks SME will guide data scientists, analysts, infrastructure specialists, and business SMEs to deliver production-ready data solutions. This position focuses on workspace automation, onboarding support, data platform usability, infrastructure provisioning, and white-glove customer success to ensure VA users can rapidly adopt and leverage modern data tools. This position is fully remote within the United States. - Implement and manage linked services, mount points, and catalog configuration within Databricks and VA’s VHA Data Lake. - Develop and optimize PySpark (Python/Scala) jobs, notebooks, and workflows in Databricks. - Build ETL/ELT pipelines using Azure Databricks Lakehouse features. - Design and implement Spark-based data processing for analytics workloads. - Optimize Databricks Spark jobs for performance, cost, and scalability (partitioning, caching, tuning, etc.). - Collaborate with data scientists, analysts, infrastructure specialists, and business SMEs to deliver production-ready data solutions. - Experience with Delta Lake and Synapse. - Ensure data quality, governance, and security best practices. - Act as subject matter experts to the workgroups from start to finish in their migration to the cloud. - Help workgroups set up their resources and migrate their workloads. - Support automated data warehouse access provisioning for eligible users, ensuring seamless integration with VA Cloud Data Warehouse (CDW) systems. - Provide white-glove customer success services, including onboarding sessions, office hours, rapid async support, and creation of reusable templates, guides, and best-practice documentation. - Collaborate with Customer Success, Business Analysts, and Platform Engineering teams to refine intake processes, improve user satisfaction, and standardize workspace deployment. - Develop migration guidance and self-service resources to accelerate platform adoption and reduce user friction when transitioning from legacy data systems. - Troubleshoot user issues related to data access, workspace configuration, pipelines, cataloging, or permissions, escalating to engineering teams when needed. Qualifications - Bachelor’s degree in Information Technology or a related field (or commensurate experience). - 12+ years of experience in business analysis, project management, or a similar role. - Strong experience with Databricks and Azure data services, including workspace administration, catalog creation, linked services, storage mounts, and access configuration. - Proficiency in data engineering fundamentals such as ETL/ELT pipeline development, data modeling, and managing structured and unstructured datasets. - Ability to automate provisioning workflows and infrastructure using scripting or infrastructure-as-code tools (Python, PowerShell, Bash, Terraform, ARM, or Bicep). - Strong troubleshooting and problem-solving abilities for diagnosing platform, data access, and pipeline issues and collaborating across teams for resolution. - Effective communication skills with the ability to document technical processes, create reusable templates, and support users through onboarding, office hours, and white-glove assistance. Requirements - Experience with Data Analysis, Data Curation, Data Visualization (PowerBi), Python, Azure Synapse/Azure Data Factory, Azure Data Lake Storage, SQL Server. - Certifications: Azure Databricks and Spark for Data Engineers, PySpark and SQL, Microsoft Azure Cert, SQL, SQLlite, Python Pandas, Sybase/SAP PowerDesigner, Databricks DAB, Databricks Unity Catalog, and Databricks lakeflow pipelines. - Exposure to Starburst, PowerBI, SSRS, SSIS, and Unity Catalog. - Familiarity with modern data lakehouse architectures, distributed data systems, and cloud data engineering practices. - Strong understanding of Azure data services, including storage, compute, identity and access management, and DevOps workflows. - Experience with workflow automation, CI/CD pipelines, or infrastructure-as-code (e.g., Terraform, ARM, Bicep). - Background in user-facing or customer-success-adjacent work such as onboarding, training, communications, or technical support. - Ability to translate business needs into technical requirements and develop scalable solutions that improve platform efficiency. - Knowledge of enterprise data systems common to healthcare or federal environments, especially data governance and data-security considerations. - Experience with tools such as Jira, ServiceNow, or SharePoint for intake, change management, and support processes. Clearance Required - Ability to obtain and maintain a suitability/Public Trust. Posted Salary Range USD $175,000.00 - USD $195,000.00 /Yr.