Key Responsibilities and Required Skills for Business Intelligence Manager

🎯 Role Definition

The Business Intelligence (BI) Manager leads analytics strategy, reporting, and self-service BI across the organization. This role is accountable for designing and delivering scalable dashboards and analytics solutions, managing a team of BI analysts and developers, partnering with business stakeholders to define KPIs, and governing data quality and access. The BI Manager combines technical expertise (SQL, ETL, data modeling, BI tools such as Power BI, Tableau, Looker) with strong stakeholder management and product sense to translate business problems into measurable analytics outcomes.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior BI Analyst or BI Developer
Data Analyst or Analytics Consultant
Reporting Manager or Business Analyst

Advancement To:

Director of Business Intelligence / Director of Analytics
Head of Data & Analytics
VP of Data & Analytics or Chief Data Officer (CDO)

Lateral Moves:

Data Product Manager
Analytics Strategy Lead
Data Governance or Data Engineering Lead

Core Responsibilities

Primary Functions

Lead the BI team to design, build, and maintain enterprise-grade dashboards and visualizations that provide timely insights into revenue, customer behavior, product performance, and operational KPIs; own the end-to-end lifecycle from requirements gathering through deployment and maintenance.
Define and operationalize the analytics roadmap and backlog, prioritizing BI initiatives that deliver measurable business value, reduce time-to-insight, and support strategic company objectives.
Partner with business leaders (Sales, Marketing, Finance, Product, Operations) to translate ambiguous business problems into clear metrics, analytic specifications, and success criteria; deliver actionable recommendations rather than just reports.
Architect and enforce a best-practice BI stack: data modeling standards, naming conventions, reusable semantic layers, and standardized calculations to ensure consistency across dashboards and reports.
Oversee design and implementation of data warehouse/data lake integrations, ensuring ETL/ELT pipelines (dbt, Fivetran, Stitch, Airbyte, Informatica) reliably ingest, transform, and expose accurate data for analytics consumers.
Manage and mentor a team of BI analysts, data visualization engineers, and report developers—conduct performance reviews, set professional development plans, hire top talent, and create a high-performing analytics culture.
Own data quality and lineage initiatives: implement monitoring, validation tests, reconciliations, and exception handling so stakeholders trust the data and insights produced by the BI function.
Build and maintain automated reporting processes and scheduled data refreshes to guarantee near real-time or day-old insights as required by the business.
Drive adoption of self-service analytics by designing intuitive dashboards, creating training materials, hosting office hours, and enabling business users to explore data safely and independently.
Define KPIs and OKRs across functions, implement measurement frameworks, and create executive-level scorecards and monthly/quarterly performance reports that inform leadership decisions.
Implement governance, security, and access controls for BI artifacts and underlying data sources, collaborating with IT and security teams to adhere to compliance and privacy requirements (GDPR, CCPA, HIPAA where applicable).
Evaluate, select, and manage BI tooling and vendor relationships (Power BI, Tableau, Looker, Qlik, ThoughtSpot) including total cost of ownership, ROI, and roadmap alignment with organizational needs.
Collaborate with Data Engineering to optimize schemas, indexing, and query performance; advise on partitioning, clustering, and aggregation strategies to improve dashboard performance and reduce cost in cloud warehouses (Snowflake, BigQuery, Redshift).
Lead cross-functional analytics projects such as pricing analysis, churn modeling, funnel optimization, campaign measurement, and capacity planning—partnering with Data Science to operationalize advanced analytics where needed.
Translate analytic results into succinct executive communications: slide decks, one-page summaries, and data-backed recommendations that influence strategy and operational decisions.
Create and maintain documentation for data definitions, report catalogs, and analytics standards that support knowledge transfer, onboarding, and auditability.
Conduct regular stakeholder reviews to validate dashboard effectiveness, gather feedback, and iterate on visualizations and metrics to ensure continued relevance and clarity.
Manage BI budget, resource planning, and project timelines—balance tactical requests with strategic platform improvements and refactors to avoid technical debt.
Set and measure SLAs for report delivery, incident response, and data accuracy; implement post-mortems and continuous improvement cycles for recurring issues.
Champion data literacy across the organization by running training sessions, analytics bootcamps, and best-practice forums; foster a data-driven decision-making culture.
Monitor industry BI trends and emerging analytics capabilities (augmented analytics, embedded analytics, NLP-driven analytics) and pilot relevant technologies to keep the organization competitive.
Prepare for and support audit and compliance reviews related to reporting and data handling, providing artifact evidence and remediation plans when necessary.

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.
Assist in vendor evaluation and proof-of-concept exercises for new BI tools or data platforms.
Facilitate cross-training between BI, Data Engineering, and Data Science to reduce single points of failure.
Help define retention policies and data archival strategies to manage long-term storage costs and historical analytics needs.
Represent the BI function in cross-organizational committees or working groups focused on analytics governance and enterprise reporting standards.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL skills for complex querying, window functions, subqueries, performance tuning, and query optimization across modern data warehouses (Snowflake, BigQuery, Redshift).
Hands-on experience with BI/reporting tools such as Power BI, Tableau, Looker, Qlik, or MicroStrategy; ability to design high-impact dashboards and reusable semantic models.
Strong data modeling expertise: star/snowflake schemas, dimensional modeling, slowly changing dimensions, fact and dimension design to support performant analytics.
Practical knowledge of ETL/ELT processes and tools (dbt, Airflow, Fivetran, Talend, Informatica) including transformation testing and orchestration best practices.
Familiarity with cloud data platforms and architecture patterns: Snowflake, BigQuery, Redshift, Azure Synapse, and associated security/configuration features.
Scripting and automation: experience with Python or R for data manipulation, automation of reporting pipelines, or integration with analytics workflows.
Metrics and KPI definition: proven ability to define, document, and operationalize business metrics with rigorous definitions and edge-case handling.
Performance monitoring and observability: use of query profilers, dashboard performance analytics, and instrumentation to maintain acceptable SLAs.
Data governance, access control, and compliance experience—implementing role-based access, anonymization, and lineage tools where required.
Familiarity with version control for analytics artifacts (Git for dbt, LookML, or BI project files) and CI/CD concepts for deployment of dashboards and models.
Experience integrating analytics into operational systems and workflows (embedded analytics, API-driven reporting).
Basic understanding of statistics and experimentation (A/B testing) to interpret results and support data-driven product decisions.

Soft Skills

Strong stakeholder management and communication skills: able to synthesize complex analyses into clear, actionable narratives for executives and non-technical audiences.
Leadership and people management: hiring, coaching, performance management, and building a collaborative team culture.
Strategic thinking and prioritization: balancing urgent business requests with long-term platform health and feature roadmap.
Problem solving and critical thinking: diagnosing root causes of data issues and designing pragmatic solutions under ambiguity.
Cross-functional collaboration: proven success working with Product, Engineering, Finance, and Operations to align analytics with business goals.
Project management and delivery orientation: ability to scope work, set milestones, and deliver high-quality analytics on time.
Change management and training: driving adoption of new BI tools and processes while minimizing disruption to business users.
Attention to detail and quality focus: establishing QA processes, data validation checks, and clear documentation standards.
Influencing and negotiation: gaining alignment on metrics, tooling choices, and resource allocation across stakeholders.
Curiosity and continuous learning: staying current on analytics methodologies, visualization best practices, and emerging BI technologies.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Systems, Mathematics, Statistics, Business Analytics, Finance, Economics, or a related quantitative discipline.

Preferred Education:

Master's degree in Business Analytics, Data Science, MBA, Information Systems, or equivalent professional experience.
Certifications such as Tableau Desktop Specialist, Microsoft Certified: Data Analyst Associate (Power BI), Looker LookML Developer, or Snowflake certifications are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

5–10+ years in analytics, business intelligence, or data roles with at least 2–4 years managing BI teams or leading cross-functional analytics programs.

Preferred:

Proven track record delivering enterprise BI solutions, building data teams, and driving measurable business outcomes through analytics. Experience in cloud-native data stacks, modern BI tooling, and strong stakeholder-facing delivery is highly desirable.