• 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.

Role Description Responsible for all the aspects of architecture, design and implementation of Data Management solution using Big Data platform on Cloudera or Hortonworks and other areas of enterprise application platforms. Reporting and analytics are very important for this position. Payment integrity is also important. Please update your resume with any relevant previous experience. - Convert concepts to technical architecture, design and implementation - Provide guidance on choosing ideal Architecture, Evaluating tools and Frameworks, define Standards & Best Practices for implementing scalable business solutions - Implement Batch and Real-time data ingestion/extraction processes through ETL, Streaming, API, etc., between diverse source and target systems with structured and unstructured datasets - Design and build data solutions with an emphasis on performance, scalability, and high-reliability - Code, test, and document new or modified data systems to create robust and scalable applications for data analytics - Build data model for analytics and application layers - Contribute to leading and building a team of top-performing data technology professionals - Help with project planning and scheduling - Expert level experience on Hadoop cluster components and services (like HDFS, YARN, ZOOKEEPER, AMBARI/CLOUDERA MANAGER, SENTRY/RANGER, KERBEROS, etc.) - Ability to participate and lead, in solving technical issues while engaged with infrastructure and vendor support teams. Qualifications - Bachelor's Degree - 5-7 years of data management experience. - Experience in building stream-processing systems, using solutions such as Kafka, Storm or Spark-Streaming. - Proven experience on Big Data tools such as, Spark, Hive, Impala, Polybase, Phoenix, Presto, Kylin, etc. - Experience with integration of data from multiple data sources (using ETL tool such, Talend, etc.). - Experience building solutions with NoSQL databases, such as HBase, Memsql. - Strong experience on Database technologies, Data Warehouse, Data Validation & Certification, Data Quality, Metadata Management and Data Governance. - Experience with programming language such as, Java/Scala/Python, etc. - Experience implementing Web application and Web Services APIs (REST/SOAP). Requirements - Master's Degree (Preferred) - 7-10 years of data management experience (Preferred) - Experience in the healthcare industry is preferred. - Reporting and analytics are very important for this position. - Payment Integrity experience is very important for this opportunity. Benefits Molina Healthcare offers a competitive benefits and compensation package. Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V.

Role Description Impact You Will Make in the Role: - Own Databricks production support for the company's data platform, including monitoring, alerting, and incident response across all production data flows. - Maintain and report on SLA performance metrics for data pipeline delivery, ensuring visibility into platform health and accountability across internal and external stakeholders. - Identify and implement pipeline optimizations that reduce Databricks compute costs, improve throughput, and reduce processing windows while tracking impacts through measurable KPIs. - Migrate legacy ETL/ELT pipelines to Databricks, building automation tooling to reduce manual intervention and ensure uninterrupted data delivery during transitions. - Support new customer onboarding by provisioning, validating, and hardening tenant data pipelines that deliver reliable, isolated data from day one. - Design and build high-performance Databricks pipelines that ingest, transform, and serve ERP and CRM data at scale across both Azure and AWS environments. - Own the Delta Lake architecture, including schema design, partitioning strategies, data quality enforcement, and incremental processing patterns. - Enforce data security best practices across Databricks environments, including role-based access control, secrets management, and compliance requirements for enterprise business data. - Implement data quality monitoring and observability across pipeline health and ML model inputs, ensuring data integrity that directly supports predictive analytics. - Apply and enforce multi-tenant data isolation patterns, ensuring reliable and secure data delivery across enterprise customers. - Partner with the Enterprise Architecture team to ensure data pipelines integrate seamlessly with the broader AI and analytics ecosystem. - Support a globally distributed operation through on-call rotation and after-hours incident response, meeting SLAs across multiple time zones. - Maintain technical documentation, runbooks, and architectural decision records, contributing to team knowledge sharing and operational readiness across on-call and incident response scenarios. - Apply CI/CD best practices to data pipeline development, including version control, automated testing, and deployment tooling to ensure reliable and repeatable pipeline delivery. Qualifications - 4+ years of data engineering experience. - At least 2 years of experience with Databricks or the Apache Spark ecosystem across Azure and/or AWS. - Proficiency in PySpark, SQL, and Python with a strong track record of building and operating production-grade pipelines under SLA constraints. - Hands-on experience with Delta Lake, including schema evolution, ACID transactions, optimize/vacuum lifecycle, and both incremental and streaming processing patterns. - Hands-on experience with pipeline performance tuning and compute optimization in production Databricks environments. - Solid working knowledge of PostgreSQL, including query optimization, schema design, and use as a source or sink in production data pipelines. - Experience supporting and maintaining legacy ETL tooling (SSIS, Informatica, custom Python/SQL pipelines, or similar) in production. - Experience supporting large-scale multi-tenant architectures with a focus on tenant isolation, per-tenant performance, and data privacy, including navigating tools and platforms that default to single-tenant assumptions. - Proven ability to work collaboratively across data science, product, and infrastructure teams, owning end-to-end delivery in a cross-functional environment. - Strong understanding of data governance, security, and compliance principles, including access control, data privacy, and protection of sensitive enterprise data across multi-tenant environments. Preferred Qualifications / Experience - Experience operating Databricks workspaces across both Azure and AWS, including cost governance, cluster management, and cross-cloud data access. - Experience optimizing Databricks workloads in a Serverless environment, including compute cost governance and performance tuning for serverless compute. - Experience with Microsoft SQL Server in a data engineering or ETL context. - Exposure to ML feature engineering or feature stores (Databricks Feature Store, Feast, or similar) supporting predictive analytics. - Experience with customer onboarding automation or Infrastructure as Code (IaC) patterns for provisioning tenant data pipelines at scale. - Databricks Certified Data Engineer Associate or Professional certification.