Jobs

Key Responsibilities  Perform data discovery, profiling, source-to-business mapping, and analytical requirements translation.  Design and optimize SQL datasets, dimensional/semantic models, views, and materialized views with clear grain and aggregation rules.  Build and maintain governed Databricks Metric Views, including measures, dimensions, relationships, and business definitions.  Configure and improve Genie Agents/Spaces with instructions, sample questions, trusted SQL patterns, and benchmark questions.  Test answer accuracy, business semantics, performance, and consistency; diagnose fan-out, double counting, and incorrect responses.  Implement and evaluate cross-domain Genie Agent routing/orchestration and integration patterns as required.  Partner with business stakeholders, data product teams, and Solution Architecture to deliver reusable, production- ready solutions. Required Technical Skills SQL, Data Discovery & Modeling  Advanced SQL: joins, CTEs, window functions, aggregation, deduplication, date logic, and query optimization.  Data profiling: grain, cardinality, nulls, freshness, relationships, historical/SCD behavior, and data quality.  Dimensional and semantic modeling: facts, dimensions, conformed dimensions, KPI definitions, aggregation correctness, and fan-out prevention. Databricks Semantic Layer  Hands-on Databricks SQL, Unity Catalog, Delta Lake, views, and materialized views.  Design and maintenance of Databricks Metric Views: measures, dimensions, joins/entities, and governed business definitions.  Understanding of data access controls, lineage, performance, and semantic consistency across consumption tools. Genie & Conversational Analytics  Hands-on Databricks Genie Agent/Space configuration, semantic grounding, instructions, and curated question patterns.  Benchmark testing, SQL/answer validation, troubleshooting, and iterative tuning for trusted business responses.  Ability to assess multi-agent routing and orchestration patterns for cross-domain analytical questions. Engineering Practices  Git-based development, testing, documentation, release discipline, and collaboration with engineering teams.  Ability to explain complex analytical logic and results clearly to business and technical stakeholders. Additional Skills (Nice to Have)  dbt, Python, REST APIs, MCP integration, and Databricks Apps/Streamlit. the  Experience implementing Genie One or custom supervisor-agent orchestration across multiple domains.  Power BI/Tableau semantic models and enterprise-wide KPI alignment.  Experience with reusable enablement frameworks, CI/CD automation, and Databricks cost optimization. Ideal Contractor Profile Hands-on engineer with 5+ years in data engineering, analytics, BI, or data platforms who can independently turn unfamiliar business data into governed semantic models and accurate, production-ready Genie experiences. Combines strong SQL and analytical judgment with business understanding, rigorous validation, and a reusable engineering mindset.