Business Intelligence and Analytics - Manager

Job Overview:

Qualifications

• Required qualifications
• Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, Business or a related field (or equivalent experience).
• 7+ years in BI, analytics or reporting, including 2+ years leading or managing a team.
• Hands-on expertise with at least one major BI platform (Power BI, Tableau, Looker or Qlik), including data modeling and dashboard design.
• Strong SQL skills and a solid grasp of data warehousing concepts (star schema, ETL/ELT, semantic layers).
• Proven record of designing and delivering executive-level reports and analytics that drove business decisions.
• Working knowledge of how GenAI and LLMs apply to reporting and analytics, with practical exposure through a pilot, project or tool adoption.
• Excellent communication: able to explain complex data to non-technical executives.
• Preferred qualifications
• Master's degree or MBA.
• Experience with AI features in BI tools (for example Copilot in Power BI, Tableau AI, Gemini in Looker) or with LLM APIs for reporting use cases.
• Familiarity with prompt design, retrieval-augmented generation (RAG) and evaluating AI output quality.
• Experience with cloud data platforms such as Snowflake, Databricks, BigQuery, Azure Synapse or Microsoft Fabric.
• Python or R for analysis and automation.
• Knowledge of data governance, privacy and responsible-AI practices.
• Relevant certifications (for example Microsoft PL-300, Tableau Certified, or a cloud data certification).
• Skills and competencies
• Leadership: builds and motivates teams, delegates well, gives clear feedback.
• Business acumen: links metrics to revenue, cost and customer outcomes.
• Data storytelling: turns analysis into a clear message and a recommended action.
• Stakeholder management: negotiates priorities and sets expectations with senior leaders.
• Innovation mindset: tests new tools such as GenAI with a clear view of value and risk.
• Rigor: insists on data quality, reconciliation and documented definitions.

Responsibilities

• BI and reporting strategy
• Define and own the BI roadmap, aligned to business priorities and KPIs.
• Standardize metrics and reporting so every team works from one trusted version of the numbers.
• Decide what to build, buy or retire across dashboards, semantic models and reports.
• Analytics and report delivery
• Oversee design and delivery of executive dashboards, operational reports and self-service analytics.
• Translate business questions into clear analysis, recommendations and data stories for senior leaders.
• Ensure reports are accurate, timely, documented and easy to use.
• GenAI-enabled modern reporting
• Identify and pilot GenAI use cases in reporting: natural-language queries, automated commentary, anomaly explanations and report summarization.
• Evaluate AI features in BI platforms and LLM-based tools; run proofs of concept and scale what works.
• Set guardrails for responsible AI use, including data privacy, accuracy checks and human review of AI-generated insights.
• Train analysts and business users to use AI-assisted reporting effectively.
• Team leadership
• Hire, coach and develop analysts and BI developers; set goals and run performance reviews.
• Prioritize the team's backlog and manage delivery against deadlines and quality standards.
• Build a culture of curiosity, data literacy and continuous improvement.
• Stakeholder partnership and governance
• Act as the main point of contact for business leaders on reporting and analytics needs.
• Work with data engineering on data models, pipelines and quality.
• Enforce data governance: access controls, definitions, lineage and compliance requirements.

Preferred Qualifications

• Master's degree or MBA.
• Experience with AI features in BI tools (for example Copilot in Power BI, Tableau AI, Gemini in Looker) or with LLM APIs for reporting use cases.
• Familiarity with prompt design, retrieval-augmented generation (RAG) and evaluating AI output quality.
• Experience with cloud data platforms such as Snowflake, Databricks, BigQuery, Azure Synapse or Microsoft Fabric.
• Python or R for analysis and automation.
• Knowledge of data governance, privacy and responsible-AI practices.
• Relevant certifications (for example Microsoft PL-300, Tableau Certified, or a cloud data certification).
• Skills and competencies
• Leadership: builds and motivates teams, delegates well, gives clear feedback.
• Business acumen: links metrics to revenue, cost and customer outcomes.
• Data storytelling: turns analysis into a clear message and a recommended action.
• Stakeholder management: negotiates priorities and sets expectations with senior leaders.
• Innovation mindset: tests new tools such as GenAI with a clear view of value and risk.
• Rigor: insists on data quality, reconciliation and documented definitions.