BI Lead

EXL•Jersey City, NJ

8d•$140,000 - $160,000

About The Position

The BI Lead serves as the primary technical authority on data and analytics platforms, leading BI architecture and solution design conversations with clients. The role designs, builds, and optimises enterprise-scale Power BI semantic models and Tableau data sources, and creates high-performance reporting through advanced DAX, M/Power Query, and SQL. It diagnoses data-model and upstream data-quality issues, architects dimensional models and shared semantic layers, and establishes self-service BI capabilities through certified datasets, governed metrics, and business-user enablement. The role also defines BI deployment standards, CI/CD pipelines, release governance, and data governance frameworks; partners with data engineering teams; and mentors BI developers through reviews and best-practice guidance. The posted base compensation range is 140,000–160,000. Final offers may vary based on skills, experience, internal pay equity, work location, market conditions, and role scope. Compensation may exceed the posted range in higher-cost zones. Benefits information is available through the company careers and benefits website.

Requirements

Bachelor’s degree in engineering or a related field.
Power BI semantic-model design and optimisation, including star-schema modelling, cardinality reduction, relationship management, aggregations, and composite models.
Advanced DAX, including efficient reusable measures, evaluation context, CALCULATE, iterator functions, and time-intelligence patterns.
Advanced M/Power Query transformations, query folding, incremental refresh, and parameter-driven pipelines.
Power BI Service governance, including certified datasets, deployment pipelines, workspaces, row-level security, gateways, and refresh scheduling.
Self-service BI experience promoting dataset reuse and enabling business users to build reports independently of IT.
Tableau dashboard and data-source design for enterprise-scale reporting.
Tableau Server or Tableau Cloud governance, published data sources, and performance optimisation.
Strong SQL skills, including complex queries, CTEs, window functions, query optimisation, and reading execution plans.
Data warehouse and dimensional modelling, including fact/dimension design, schema validation, and data lineage.
Understanding of ETL/ELT and ability to diagnose and resolve upstream data issues affecting BI layers.
Ability to present architecture, data strategy, and roadmaps to client executives and technical leaders.
Ability to gather requirements from non-technical stakeholders and translate them into scalable BI solutions.
Experience leading architecture reviews, discovery workshops, and solution-design sessions.

Nice To Haves

Snowflake experience, including query optimisation, warehouse sizing, and integration with Power BI or Tableau through DirectQuery or native connectors.
Sigma Computing experience with 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 governance gaps or data duplication.
Working knowledge of insurance policy lifecycle, claims, underwriting, premiums, loss ratios, and combined ratio.
Ability to translate insurance business questions into structured KPIs, metrics hierarchies, and dashboard designs.

Responsibilities

Lead BI architecture and solution design conversations with clients and act as the primary technical authority on data and analytics platforms.
Design, build, and optimise 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 for accurate, high-performance reporting.
Diagnose and resolve data-model issues, including slow DAX, broken relationships, cardinality problems, and upstream data-quality issues.
Architect dimensional data models, including star schemas and fact/dimension designs, and semantic layers used across multiple BI tools.
Promote self-service BI by certifying datasets, defining governed metrics, and building enablement programmes for business teams.
Define BI deployment standards, CI/CD pipelines, and release governance for 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 standardisation across business units.
Mentor BI developers through code and model reviews and share DAX, SQL, and design best practices.