Optimize pipelines for reliability, performance, scalability, and cost.
Build a configuration-driven source onboarding framework and Lakeflow pipeline templates.
Automate source registration, deployment, monitoring, lineage, and operational controls.
Implement data quality expectations, anomaly detection, monitoring, and quarantine processes.
Implement job and pipeline monitoring, alerting, audit logging, and operational dashboards.
Support automated deployment and promotion using Databricks Asset Bundles and CI/CD.
Develop conformed dimensions and shared-key strategies across domains.
Build integrated Gold-layer products and reusable data contracts.
Build AI-ready Gold data products with ownership, lineage, definitions, and quality information.
Develop semantic context, business glossary, ontology, and certified KPIs in Unity Catalog.
Build governed Unity Catalog functions and agent-ready tools and endpoints.
Implement tracing, evaluation, testing, monitoring, and governance for AI and agent workflows.
Support development of AI agents that use trusted enterprise data.
Experience Requirements
Advanced hands-on experience with Databricks, Apache Spark, Delta Lake, and SQL.
Hands-on experience with AWS services related to data analytics and AI, including EMR and Bedrock.
Strong Python and SQL development skills.
Experience building scalable batch and/or streaming data pipelines.
Deep understanding of Bronze/Silver/Gold medallion architectures.
Experience with Databricks Lakeflow or comparable orchestration technologies.
Experience implementing enterprise data quality, lineage, observability, and monitoring.
Experience with Unity Catalog and governed enterprise data environments.
Strong understanding of dimensional modeling, data products, semantic models, and cross-domain integration.
Experience with Git-based development, CI/CD, automated testing, and production deployment.
Ability to troubleshoot complex distributed data and pipeline issues.
Experience building AI and GenAI solutions against enterprise data.
Understanding of semantic layers and natural-language-to-data architectures.
Experience with Databricks Genie or comparable natural-language analytics technology.
Experience building tools, functions, APIs, or services consumed by AI agents.
Understanding of MCP, agent tool calling, tracing, evaluation, and AI governance.
Experience developing production AI solutions with security, observability, and governance controls.
Preferred: Databricks Data Engineer Professional or equivalent experience.
Preferred: Experience with Databricks AI/BI, Genie, MLflow, and MCP-based integrations.
Preferred: Experience in Real Estate, Finance, People/Workplace, Operations, or similar domains.
Preferred: Experience building metadata-driven or configuration-driven ingestion frameworks.
