Location: CDMX / Remote

Kohler Opportunity

Role Summary:

Join Kohler’s AI Innovation team – Enterprise Architecture Office team to design and deliver Generative Business Intelligence (GenBI) / Conversational AI solutions that enable trusted, self service analytics and AI assisted insights. As a GenBI Engineer I, you will focus on enterprise data modeling, certified data assets, and metadata enrichment, while building conversational and GenAI powered BI experiences using Databricks Genie, Microsoft Copilot and/or Fabric Data Agents.
You will work extensively with Databricks, Unity Catalog, and/or Microsoft Fabric - One Lake, contributing to standardized semantic models aligned with the Common Data Model (CDM). The role emphasizes data trust, governance, discoverability, and AI ready datasets that power dashboards, copilots, and embedded analytics across the enterprise.

Key Responsibilities:

• Design, build, and maintain enterprise grade data models (facts, dimensions, and semantic layers) optimized for BI and GenAI consumption.
• Develop and manage certified data assets in Databricks and/or Microsoft Fabric OneLake, aligned to the Common Data Model (CDM).
• Implement metadata enrichment and governance using Databricks Unity Catalog, including business metadata, data classifications, ownership, lineage, and certification.
• Build GenBI applications using Databricks Genie, Microsoft Copilot and/or Fabric Data Agents, enabling natural language analytics, conversational BI, and insight generation.
• Hands on building SQL expressions within Databricks Genie to improve query processing, reduce latency.
• Creating benchmarks and instructions within Databricks Genie/ MSFT CoPilot to improve grounding and accuracy of Conversational AI models.
• Partnering with Cybersecurity to create right AD groups, access management and AD group mapping to Genies.
• Partner with data engineers to curate gold layer datasets (Delta tables / Lakehouse models) that are analytics and AI ready.
• Develop semantic definitions, KPIs, and metrics to ensure consistent enterprise reporting and AI interpretations.
• Enable self service analytics by publishing governed datasets and models for Power BI, Databricks SQL, and Copilot experiences.
• Apply data quality, validation, and observability checks to ensure accuracy, freshness, and reliability of certified datasets.
• Support responsible AI and data governance practices, including PII classification, access controls, and auditability.
• Document data products, models, and GenBI use cases, including business definitions and success metrics.

Qualifications:

• 4–6 years of experience in data engineering, BI engineering, analytics engineering, or strong academic/portfolio projects.
• Strong SQL skills and proficiency in Python for data transformation and validation.
• Experience or exposure to data modeling concepts (star schema, snowflake, semantic models, metrics layers).
• Familiarity with Databricks (Delta Lake, Databricks SQL) and/or Microsoft Fabric (One Lake, Warehousing) and proven track record of building GenBI application/Conversational analytics leveraging Databricks Genie.
• Understanding of metadata, data governance, and cataloging concepts.
• Experience with Git based version control and CI/CD for data pipelines or analytics artifacts.
• Strong analytical thinking, problem solving, and ability to collaborate with business and technical stakeholders.

Preferred Experience:

• Hands on experience with Databricks Unity Catalog for data governance, lineage, and certification.
• Experience building and consuming Common Data Model (CDM) aligned datasets.
• Exposure to Databricks Genie, Microsoft Copilot, or conversational BI models and tools.
• Knowledge of BI tools such as Power BI or Databricks SQL dashboards.
• Familiarity with enterprise domains such as Finance, Procurement, HR, Supply Chain, or Manufacturing analytics.
• Understanding of data quality frameworks, KPI definitions, and metric governance.
• Relevant certifications (e.g., Databricks Data Engineer, Fabric Analytics Engineer, Power BI).

Success Metrics:

• Delivery of certified, well documented data assets adopted by BI and GenAI use cases.
• Demonstrated improvements in data trust, discoverability, and self service analytics adoption.
• Successful enablement of GenBI experiences with accurate, explainable, and governed results.
• Adherence to enterprise architecture, data governance, security, and responsible AI standards.