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Key Responsibilities and Required Skills for Business Intelligence Lead

💰 $ - $

Business IntelligenceAnalyticsDataLeadershipBI

🎯 Role Definition

The Business Intelligence Lead is responsible for defining and executing the organization's BI and analytics strategy, leading an experienced team of BI analysts and engineers, and delivering actionable insights through modern data platforms, dashboards, and self-service analytics. This role partners closely with business stakeholders and engineering teams to translate strategy into measurable KPIs, scalable data models, and automated reporting that drive revenue, cost optimization, and operational efficiency. The ideal candidate combines deep technical expertise (SQL, data warehousing, ETL, visualization tools) with proven leadership in stakeholder management, analytics product delivery, and data governance.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior BI Analyst or BI Developer with ownership of dashboards and data models
  • Analytics Manager leading cross-functional reporting programs
  • Data Engineer focused on analytics platforms and ETL pipelines

Advancement To:

  • Director of Business Intelligence
  • Head of Analytics / Head of Data & Insights
  • VP of Data & Analytics

Lateral Moves:

  • Data Engineering Manager
  • Product Analytics Lead
  • Analytics Consulting / BI Architect

Core Responsibilities

Primary Functions

  • Lead the end-to-end delivery of BI products — including requirements discovery, data model design, ETL orchestration, visualization, testing, deployment, and ongoing operational support — to provide executives and business users with reliable, self-service analytics.
  • Own the BI roadmap: prioritize analytics initiatives, define success metrics, estimate effort and business impact, and align the roadmap with corporate strategy and KPIs.
  • Design and implement semantic data models and dimensional schemas (star schema, slowly changing dimensions), ensuring consistency across dashboards and analytic workloads to enable accurate, high-performance reporting.
  • Architect and govern the company’s data warehouse / lakehouse strategy (e.g., Snowflake, BigQuery, Redshift, Synapse), collaborating with data engineering to ensure optimal storage, performance, and cost controls.
  • Manage, mentor, and grow a multidisciplinary BI team of analysts, data modelers, ETL engineers, and visualization specialists; set performance objectives, provide coaching, and run effective 1:1s and career development plans.
  • Define and enforce data governance, quality, lineage, master data management, and documentation standards that ensure trust in BI outputs across finance, sales, marketing, and operations.
  • Build and deliver executive-level dashboards and board-ready reports that synthesize complex analyses into actionable insights and concise narratives for C-suite consumption.
  • Establish a standardized BI development lifecycle (requirements, prototyping, unit tests, UAT, deployment, rollback) and implement CI/CD best practices for analytics artifacts and dashboard code.
  • Collaborate with product, finance, marketing, operations, and sales stakeholders to translate business questions into analytic specifications, meaningful KPIs, and automated reporting solutions.
  • Drive the adoption of self-service analytics by defining role-based access, reusable semantic layers, training, and documentation so business users can answer questions without engineering intervention.
  • Oversee ETL/ELT processes (dbt, Airflow, Matillion, Spark) ensuring transformations are performant, tested, and auditable; optimize data pipelines for scalability and latency SLAs.
  • Implement reporting best practices including A/B testing measurement, cohort analysis, attribution modeling, and lifecycle metrics to support growth and retention initiatives.
  • Partner with security and compliance teams to ensure analytics practices meet GDPR, CCPA, SOC2, and internal policy requirements for data handling and access control.
  • Conduct regular audits of dashboards, metrics, and SQL logic to prevent metric drift, duplicate KPIs, and misinterpretation; maintain a single source of truth for critical business metrics.
  • Lead complex ad hoc analyses and modeling efforts (forecasting, predictive models, scenario analysis) to influence strategic decisions such as pricing, inventory, and go-to-market investments.
  • Negotiate data product SLAs and prioritize service levels for different classes of reporting consumers (operational, tactical, strategic).
  • Champion the evaluation, selection, and adoption of BI and analytics tools (Power BI, Tableau, Looker, ThoughtSpot), balancing UX, governance, cost, and integration requirements.
  • Develop and track success metrics for the BI function (adoption rates, query performance, time-to-insight, reduction in manual reporting) and report on BI health to senior leadership.
  • Manage cross-functional programs to migrate legacy reports to new platforms, decommission technical debt, and consolidate reporting portfolios to reduce redundancy and licensing costs.
  • Lead vendor relationships with BI and cloud partners; evaluate proof-of-concepts, negotiate contracts, and ensure vendors deliver agreed service and roadmap features.
  • Foster a data-driven culture by running analytics workshops, stakeholder training sessions, and “office hours” that reduce the backlog of routine requests and increase analytic literacy.
  • Establish and manage budget, headcount planning, and resource allocation for BI initiatives, ensuring ROI-driven investment in analytics capabilities.
  • Troubleshoot escalated production incidents related to reporting inaccuracies, pipeline failures, or performance regressions; coordinate incident response and communicate resolution to stakeholders.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL expertise for complex analytical queries, performance optimization, and query profiling across large data sets.
  • Experience designing and maintaining cloud data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse).
  • Strong proficiency in BI visualization tools such as Power BI, Tableau, Looker, or Qlik — including dashboard design principles, performance tuning, and access controls.
  • Practical knowledge of ETL/ELT orchestration and tools (dbt, Airflow, Matillion, Fivetran, Stitch) and experience writing tested, modular transformations.
  • Data modeling skills: dimensional modeling, star/snowflake schemas, slowly changing dimensions, and schema evolution strategies.
  • Familiarity with scripting and analytics languages (Python, R) for data cleansing, advanced analysis, and lightweight automation.
  • Hands-on experience with analytics semantic layers and BI governance (metrics layer, LookML, Power BI semantic models, Materialized Views).
  • Experience with cloud platforms and services (AWS, GCP, Azure) and data engineering primitives (S3/Blob, IAM, compute scaling).
  • Knowledge of monitoring and observability for data pipelines and BI platforms (metric alerts, lineage, SLAs).
  • Experience implementing data governance, data cataloging, lineage, and metadata management tools (Collibra, Alation, Amundsen).
  • Familiarity with advanced analytics concepts: cohort analysis, LTV modeling, attribution, forecasting, and experimentation metrics.
  • Version control and CI/CD for analytics artifacts (git, CI pipelines for dbt, factory deployments).
  • Strong Excel modeling skills including pivot tables, Power Query, and DAX for financial and ad hoc analytic work.

Soft Skills

  • Strategic thinker who can translate business strategy into measurable analytics initiatives and KPIs.
  • Excellent stakeholder management and communication skills — capable of presenting technical findings to non-technical executives.
  • Proven leadership and people management experience with a track record of hiring, developing, and retaining high-performing BI teams.
  • Problem-solving and analytical mindset with attention to detail and bias for data-driven decisions.
  • Project management and prioritization skills to balance urgent requests with productized analytics delivery.
  • Collaborative approach — able to partner across engineering, product, finance, and marketing to deliver outcomes.
  • Coaching and mentoring capability to raise the organization’s analytic maturity and self-service adoption.
  • Change management skills to drive migration toward new BI platforms and deprecate legacy processes.
  • Business acumen: understands key business drivers (revenue, margins, CAC, churn) and can shape analysis to influence P&L outcomes.
  • Adaptability and curiosity, with a continuous learning mindset around emerging BI and cloud technologies.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Information Systems, Statistics, Mathematics, Business Analytics, Economics, or a related field.

Preferred Education:

  • Master’s degree in Data Science, Business Analytics, MBA with analytics focus, or related advanced degree.

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Statistics
  • Business Analytics / Management Information Systems
  • Economics / Mathematics

Experience Requirements

Typical Experience Range: 7–12+ years in analytics, business intelligence, or data engineering roles.

Preferred:

  • 10+ years of progressive analytics/BI experience with 3+ years in a leadership role managing BI teams and delivering cross-functional analytics programs.
  • Demonstrated track record building and scaling BI platforms in cloud environments, migrating from legacy reporting, and implementing data governance practices.
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