Business Intelligence Lead

Responsibilities Lead BI architecture and solution design conversations with clients, acting as the primary technical authority on data and analytics platforms. Design, build, and optimize Power BI semantic models and Tableau data sources for enterprise‑scale performance and maintainability. Write and optimise complex DAX measures, M/Power Query transformations, and SQL queries to support accurate, high‑performance reporting. Diagnose and resolve data model issues — slow DAX, broken relationships, cardinality problems, and upstream data quality issues — quickly and systematically. Architect dimensional data models (star schema, fact/dimension design) and semantic layers consumed across multiple BI tools. Drive self‑service BI capability: certify datasets, define governed metrics, and build enablement programmes so business teams can explore data independently. Define BI deployment standards, CI/CD pipelines, and release governance to ensure reliable and secure analytics delivery. Partner with data engineering teams to design analytics‑ready data structures and resolve data issues at source. Establish data governance frameworks covering data quality standards, metadata management, access controls, and KPI standardization across business units. Mentor BI developers through code and model reviews, sharing DAX, SQL, and design best practices to raise overall team capability.

MUST‑HAVE SKILLS

Power BI Semantic model design and optimisation: star‑schema modelling, reducing cardinality, managing relationships, aggregations, and composite models. Advanced DAX: efficient, reusable measures; evaluation context; CALCULATE; iterator functions; time intelligence patterns. M / Power Query: advanced transformations, query folding, incremental refresh, parameter‑driven pipelines. Power BI Service governance: certified datasets, deployment pipelines, workspaces, row‑level security, gateways, and refresh scheduling. Self‑service BI: promoting dataset reuse and enabling business users to build their own reports without IT dependency. Tableau Dashboard and data source design for enterprise‑scale reporting. Tableau Server / Tableau Cloud governance, published data sources, and performance optimisation. Strong SQL: complex queries, CTEs, window functions, query optimisation, and reading execution plans. Data warehouse and dimensional modelling: fact/dimension design, schema validation, and data lineage. ETL/ELT understanding: diagnosing and resolving upstream data issues that affect BI layers. Communication & Stakeholder Engagement Comfortable presenting architecture, data strategy, and roadmaps to client executives and technical leaders. Skilled at requirements gathering from non‑technical stakeholders and translating them into scalable BI solutions. Experience leading architecture reviews, discovery workshops, and solution design sessions.

GOOD TO HAVE

Snowflake: query optimisation, warehouse sizing, and integrating Snowflake with Power BI or Tableau via DirectQuery or native connectors. Sigma Computing: cloud‑native self‑service analytics on Snowflake; governed metrics, semantic model integration, and business user enablement. Experience connecting Sigma Computing with existing Power BI and Tableau ecosystems without creating governance gaps or data duplication. Working knowledge of policy lifecycle, claims, underwriting, premiums, loss ratios, and combined ratio. Ability to translate insurance business questions into structured KPIs, metrics hierarchies, and dashboard designs. #J-18808-Ljbffr NLB Services