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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Databricks Solutions Expert (Remote) - **Company:** GovCIO - **Location:** Atlanta, GA, United States (Remote available) - **Experience:** Experienced - **Salary:** $195,000.0 - **Contract:** Permanent contract - **Skills:** Computing Platforms, Audit Trail, Microsoft Azure, Continuous Integration, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Retention, Data Sharing, Data Virtualization, DevOps, Github, Identity and Access Management, Java Database Connectivity, Python (Programming Language), Key Management, Log Analysis, Machine Learning, SQL Azure, Network Architecture, Open Database Connectivity, Performance Tuning, Azure Active Directory, Azure Data Lake, SQL Databases, Data Streaming, Management of Software Versions, Azure Service Bus, Policy as Code, Feature Store, Data Classification, Data Ingestion, Azure Data Factory, Cloud Monitoring, Apache Spark, Pytest, Data Lakes, Pyspark, Storage Technologies, Information Technology, Data Lineage, Hashicorp, Apache Kafka, Data Management, Machine Learning Operations, Model Registry, Terraform, Domain Driven Design, Serverless Computing, Azure Resource Manager, Key Vault, Databricks - **Published:** October 1, 2026 - **Apply:** https://dejobs.org/x/x/EC0FC2FBB1ED41EAAE986B59E87D1059/job/ ## About the Role * Bachelor's degree in Information Technology or a related field. (or commensurate experience) * 12+ years of experience in data engineering/analytics; 5+ years building on cloud data platforms (Azure preferred). * 3+ years hands-on Azure Databricks (platform + pipelines) and Delta Lake. * Proven experience setting up Unity Catalog with granular governance (RLS/CLS). * Deep knowledge of Azure networking (VNets, Private Link, NSGs), identity (Entra ID), Key Vault, ADLS Gen2, Event Hubs, Azure SQL/MI, and Data Factory. * Strong Spark expertise (PySpark/SQL), Structured Streaming, performance tuning, partitioning and storage optimization. * Practical Terraform experience for Databricks/Azure resources; CI/CD with Azure DevOps or GitHub Actions. * Security-first mindset; track record implementing audit logging, policy-as-code, and compliance controls. * Excellent communication skills; ability to standardize, teach, and influence at enterprise scale. Preferred Skills and Experience: * Experience operating platforms for >500 concurrent users and 1000s of analytics teams. * Knowledge of Photon, DLT, Workflows, Lakehouse ML (MLflow, feature store), Delta Sharing. * Exposure to FinOps and chargeback models for data platforms. * Experience with Synapse/Fabric interoperability, and data virtualization patterns. * Background in data modeling (medallion, dimensional, domain-driven design) and data quality (expectations, SLAs)., * A valid photo ID must be presented during each interview * During the Hiring Process * Enhanced Biometrics ID verification screening * Background check, to include: * Criminal history (past 7 years) * Verification of your highest level of education * Verification of your employment history (past 7 years), based on information provided in your application ## Description The Databricks Solutions Expert will will define platform patterns, build reference implementations, set standards (cluster policies, Unity Catalog governance, CI/CD), and drive enablement so product, analytics, and data science teams can deliver reliable, compliant, and performant insights at scale. Platform Architecture & Design * Blueprint the Azure Databricks landing zone: workspace topology (prod/non-prod), network architecture (VNet injection, Private Link, NAT), secure connectivity to ADLS Gen2, Azure SQL/MI, Event Hubs, and other data sources. * Governance with Unity Catalog: multi-region metastores, catalog/schema/table design, data classification tiers, row/column-level security, data lineage, and cross-domain data sharing patterns (Delta Sharing). * Lakehouse foundations: Delta Lake storage design (bronze/silver/gold), medallion data flow standards, partitioning, Z-ordering, OPTIMIZE/VACUUM policies, and performance best practices (Photon, AQE). * Scalability for "thousands of teams": multi-workspace strategy, tenancy model (shared vs. dedicated), workspace baselines, cluster policy tiers, and guardrails to prevent noisy-neighbor and cost runaways. * Reliability: HA/DR strategy, regional deployments, backup/restore, versioning, and repeatable environment provisioning via Terraform. Security, Compliance & Access Control * Identity & access: Entra ID (Azure AD) SSO, SCIM user/group provisioning, service principals/managed identities, attribute-based/role-based access controls mapped to Unity Catalog. * Secrets & credentials: Key Vault-backed secret scopes, credential passthrough patterns, token hygiene (PAT governance). * Data protection: encryption at rest/in transit, private endpoints, data exfil and egress controls, policy-as-code, and audit logging to Log Analytics or secure storage. * Compliance: enforce enterprise policies (PII/PHI handling), data retention, legal hold, and regulatory reporting with auditable lineage. Engineering & Enablement * Pipelines: build DLT (Delta Live Tables) pipelines and jobs for batch and streaming (Structured Streaming) with CDC (e.g., via Auto Loader) from enterprise sources. * Performance engineering: optimize notebooks/SQL/ETL (Photon, caching, skew mitigation), tune cluster sizing, and set standards for reliable, fast jobs. * MLOps: integrate MLflow for experiment tracking, model registry, feature store (Unity Catalog), and serving patterns where appropriate. * Observability: end-to-end monitoring (jobs, clusters, UC audits), dashboarding, alerting, and SLOs; integrate with Azure Monitor/Log Analytics. * Enablement: build reusable reference accelerators (templates, example notebooks, data products), run playbooks, and conduct office hours to uplevel 1000s of teams. DevOps, Automation & Cost Management * Infrastructure-as-Code: provision workspaces, catalogs, cluster policies, and permissions via Terraform (Databricks provider), with pipelines in Azure DevOps/GitHub. * CI/CD: notebook/package deployment, testing harnesses (dbx/pytest), environment promotion, and artifact versioning. * FinOps: cost modeling, budgets/alerts, instance pools, auto-termination, serverless SQL, tagging/chargeback, and usage analytics for executive reporting. Stakeholder Management & Governance * Partner with Security, Networking, Compliance, and FinOps to codify enterprise standards. * Establish a Lakehouse Platform Council to ratify patterns and review exceptions. * Create adoption metrics, business case narratives, TCO models, and executive updates., * Databricks: Databricks Certified Data Engineer Professional, Lakehouse Fundamentals, Machine Learning Associate/Professional. * Microsoft: Azure Solutions Architect Expert (AZ-305), Azure Data Engineer (DP-203), Azure Security Engineer (AZ-500). * HashiCorp: Terraform Associate. Technical Stack & Tools * Core: Azure Databricks (Unity Catalog, Delta Lake, DLT, Workflows, Photon), ADLS Gen2, Azure Key Vault, Entra ID, Private Link. * Data Integration: Auto Loader, ADF/Synapse pipelines, Event Hubs/Kafka, JDBC/ODBC. * DevOps/Infra: Terraform (Databricks & Azure providers), Azure DevOps/GitHub Actions, dbx. * Observability: Databricks audit logs, Azure Monitor, Log Analytics, custom usage analytics. * Languages: Python (PySpark), SQL, Scala (optional). * ML: MLflow, Feature Store (Unity Catalog), model serving patterns. Clearance Required * Ability to obtain and maintain a suitability/Public Trust ## Related Videos - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Shifting Stress to Progress— Understanding DevOps to do DevOps Better](https://www.wearedevelopers.com/videos/268-shifting-stress-to-progress-understanding-devops-to-do-devops-better) - [pytest: Simple, rapid and fun testing with Python](https://www.wearedevelopers.com/videos/213-pytest-simple-rapid-and-fun-testing-with-python) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)