Senior BI and Data Architect

Job Responsibilities

• Own BI solutions across the full data stack: data ingestion and transformation (Synapse, Databricks), analytical data models, and Power BI semantic models and reports.
• Design robust, scalable BI architectures aligned with enterprise standards.
• Act as a bridge between data engineering and business analytics, ensuring consistency and reusability across reporting tools and the conversational-AI layer.
• Design and maintain analytical data models (star/snowflake) optimized for reporting, performance, and natural-language querying — using clean, business-friendly naming and well-defined keys and relationships.
• Build and own Power BI semantic models with clean business definitions, reusable measures, and strong performance characteristics on Fabric capacity.
• Develop advanced DAX measures, including time intelligence, trend and variance analysis, and booking curves and demand patterns relevant to the hospitality industry.
• Design and enable Databricks Genie for key business domains (e.g., bookings & demand, revenue management, operations) — selecting certified tables and metric views and authoring clear instructions, example SQL, and verified answers so business users receive trustworthy responses.
• Make data AI/BI-ready: maintain rich table and column descriptions, a business glossary, and synonyms/entity matching so Genie reliably maps business language to the correct fields.
• Continuously evaluate and improve answer accuracy, reviewing generated SQL and refining instructions based on user feedback.
• Where valuable, extend conversational analytics into the flow of work by embedding Genie in Microsoft Teams, or applications via the Conversation API.
• Develop data transformations using SQL and Python, and work with Databricks to prepare clean, well-modeled analytical datasets for both BI and conversational-AI consumption.
• Apply analytics-engineering best practices (layered models, clear data contracts, tested transformations).
• Work closely with business stakeholders (commercial, revenue, operations, leadership) to understand requirements, challenge assumptions, and translate needs into scalable BI and self-service analytics solutions.
• Clearly communicate data definitions, model limitations, and trade-offs and design decisions.
• Help drive data literacy and trust in BI outputs and AI-generated answers — including guidance on how to ask effective questions through prompt engineering and how to interpret results.
• Ensure data quality and consistency across BI models and the AI layer.
• Establish and enforce Unity Catalog metadata standards (table/column descriptions, tags, ownership, certification, lineage) and ensure Genie respects Unity Catalog permissions, row-level security, and column masking.
• Optimize Power BI models for Fabric capacity usage (memory, refresh strategy, query performance).
• Contribute to BI standards, best practices, and documentation.
• Own a strategy around shared/certified semantic models and metric definitions that serve as the single source of truth across Power BI and Databricks Genie.

Job Requirements

• Strong end-to-end BI experience across data engineering, modeling, and reporting.
• SQL – Expert: Able to design, optimize, and troubleshoot complex analytical queries; deep understanding of joins, CTEs, window functions, aggregations, and performance tuning; comfortable validating AI-generated SQL.
• Python – Advanced: Experience building production-grade data transformation and analytics workflows using PySpark/Pandas; strong understanding of data quality, automation, testing, and scalable data processing in Databricks.
• DAX – Strong Working Knowledge: Able to develop complex measures and KPIs, including time intelligence, trend and variance analysis; understands evaluation context, filter propagation, and performance optimization within Power BI semantic models.
• Power BI: semantic model design, performance optimization, Fabric capacity awareness.
• Databricks SQL and Unity Catalog – hands-on experience with catalogs, schemas, governance, lineage, and permissions.
• Dimensional/semantic data modeling with clean, business-friendly naming and well-defined keys and relationships (the foundation for reliable natural-language querying).
• Strong data-documentation discipline (descriptions, business glossary, certified definitions).
• Experience working with financial/accounting data.
• Proven ability to communicate effectively with business stakeholders.