Business Intelligence Analyst

Gartner•Stamford, CT

19d•$64,000 - $87,000•Hybrid

About The Position

As a BI Analyst within Gartner's Operations & Strategy team, you'll work closely with business partners to answer questions, solve problems, and deliver reporting and analytics solutions. You'll help transform complex data into clear insights that support operational and strategic decision-making across the organization. You thrive in ambiguity: asking clarifying questions, structuring unclear problems, and adapting as business needs evolve. You’re comfortable when definitions shift and requirements are incomplete, and you can translate the gray into actionable insights.

Requirements

1–3 years of business, analytics, operations, finance, or related analysis experience (internships and academic projects considered).
Experience working with stakeholders to clarify requirements, define metrics, analyze data, and communicate findings.
Experience managing multiple priorities or projects where requirements, timelines, data availability, or stakeholder needs may shift.
Hands-on experience in Power BI: building/maintaining reports, dashboards, datasets, or analytical views.
Proficiency with modern analytics and reporting tools (e.g., Excel, Power BI, Salesforce Native reporting, Salesforce CRM Analytics, Databricks, Tableau, Microsoft Fabric).
Strong Excel skills: Power Query, pivot tables, formulas, data preparation, and quality checks.
Ability to work across CRM, data platform, and BI/reporting environments, understanding how source systems, business processes, data definitions, and transformation logic affect analytical outputs.
SQL experience for data extraction, transformation, reconciliation, validation, and repeatable analysis.
Comfort working in ambiguity, including situations where data is incomplete, requirements are evolving, definitions are unsettled, or the business question changes as analysis progresses.
Strong analytical rigor and attention to detail, especially when validating data, reconciling inconsistencies, and explaining assumptions.
Critical thinking and creative problem-solving skills, including the ability to challenge assumptions, structure unclear problems, and identify the real business question behind an initial request.
Ability to distinguish between symptoms and root causes when analyzing business process issues.
Strong business process understanding and ability to connect data findings to operational decisions.
Ability to determine when analysis is directionally useful versus when additional validation is required before decisions are made.
Strong initiative and ownership, with the ability to move work forward proactively while seeking input at the right moments.
Clear verbal and written communication skills, including the ability to explain technical findings, limitations, and recommendations to non-technical stakeholders.
Effective listening and questioning skills to uncover the actual business need behind a request.
Ability to build trust with stakeholders through reliable analysis, clear assumptions, practical recommendations, and consistent follow-through.
Strong organization and time management skills, with the ability to prioritize work based on business impact, urgency, feasibility, and data readiness.
Openness to feedback, best practices, and continuous improvement.
Ability to work collaboratively across functions while maintaining accountability for analytical quality, validation, and delivery.

Nice To Haves

Python for data preparation, automation, exploratory analysis, or scalable analytical workflows.
Exposure to automation and low-code tools (e.g., Power Automate, Power Apps, Copilot Studio).
Familiarity with AI-assisted analytics tools, with an emphasis on responsible use, output review, data validation, and business context.
Experience with OPI/GSP applications.
Ability to create executive-ready outputs, including PowerPoint presentations, data visualizations, and concise written summaries.

Responsibilities

Frame ambiguous business problems into clear analytical questions.
Collaborate with stakeholders to clarify objectives, define success measures, identify decision points, and translate evolving requests into practical requirements.
Develop and refine KPIs that reflect business process reality.
Partner with business teams and SMEs to define metric logic, document assumptions, reconcile competing definitions, and ensure reporting outputs support meaningful operational decisions.
Analyze large and complex datasets to identify patterns, root causes, risks, and opportunities.
Use structured analysis to improve processes, uncover inefficiencies, and support recommendations that address business challenges.
Build decision-ready reporting and analytical outputs.
Develop reports, dashboards, datasets, and supporting analyses using tools such as Power BI, Salesforce Native reporting, Salesforce CRM Analytics, Databricks, Excel, Tableau, or comparable platforms.
Validate data before insights are shared.
Review source data, transformation logic, joins, calculations, business rules, and edge cases to ensure outputs are accurate, explainable, and trusted.
Translate data findings into practical business recommendations.
Communicate insights clearly, including what the data shows, what remains uncertain, what tradeoffs exist, and what actions should be considered.
Clarify stakeholder needs and align expectations.
Use thoughtful questioning, active listening, and clear communication to confirm requirements, surface assumptions, explain analytical choices, and keep stakeholders aligned as priorities evolve.
Manage competing priorities across cross-functional projects.
Balance urgent requests with longer-term improvements, clarify tradeoffs, communicate progress, and keep work aligned to business impact.
Improve analytical processes and reporting scalability.
Identify opportunities to automate, standardize, simplify, or redesign recurring analysis, data preparation, validation, and reporting workflows.
Use emerging tools responsibly to improve productivity and quality.
Apply automation, AI-assisted analytics, and modern data tools where appropriate, maintaining strong validation practices and human review of outputs.