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Key Responsibilities and Required Skills for Business Analytics and Reporting Analyst

💰 $ - $

Business AnalyticsReportingDataBI

🎯 Role Definition

The Business Analytics and Reporting Analyst is responsible for designing, building, and maintaining actionable dashboards and reports, ensuring data integrity across reporting systems, and translating business questions into measurable metrics and analytic solutions. This role partners closely with finance, sales, marketing, operations, and engineering teams to deliver timely insights, automate recurring reports, and drive data-informed decision-making across the organization. The ideal candidate combines strong SQL and BI tool experience (Power BI/Tableau/Looker), a solid understanding of data modeling and ETL processes, and the communication skills to present insights to technical and non-technical stakeholders.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst
  • Reporting Analyst
  • Business Intelligence (BI) Analyst

Advancement To:

  • Senior Business Analytics Analyst / Senior Reporting Analyst
  • Analytics Manager / BI Manager
  • Head of Business Intelligence / Director of Analytics

Lateral Moves:

  • Product Analyst
  • Financial Planning & Analysis (FP&A) Analyst
  • Operations Analyst

Core Responsibilities

Primary Functions

  • Design, develop, and maintain enterprise dashboards and executive reports using Power BI, Tableau, Looker or equivalent tools, ensuring visualizations clearly communicate KPIs and trends to business stakeholders.
  • Own end-to-end reporting pipelines: gather requirements, design data models, write performant SQL, implement transformations in ETL/ELT frameworks, and deploy reports to production.
  • Translate high-level business questions into detailed analytic plans, creating hypotheses, defining success metrics, and delivering actionable recommendations backed by data.
  • Build and validate data models and semantic layers to ensure consistent metrics across dashboards and ad-hoc analyses, including star schemas, conformed dimensions, and fact tables.
  • Develop and optimize complex SQL queries and stored procedures for large-scale data warehouses (e.g., Snowflake, BigQuery, Redshift) to support near-real-time and historical reporting.
  • Implement and maintain robust data quality checks, reconciliations, and data validation routines to detect anomalies, resolve discrepancies, and document root-cause analyses.
  • Automate recurring reports and reporting workflows using scripting (Python/R), scheduling tools, or BI tool automation features to reduce manual effort and increase accuracy.
  • Collaborate with product, finance, marketing, sales, and operations teams to define and operationalize business KPIs (e.g., CAC, LTV, churn, ARR/MRR) and build reliable measurement frameworks.
  • Perform cohort analyses, funnel analyses, segmentation, and lifetime value modeling to surface growth opportunities and retention issues.
  • Design and execute A/B test analysis and experimentation reporting, including power calculations, metric definitions, and post-test interpretation.
  • Monitor dashboard performance and implement query optimizations, incremental refresh strategies, and caching to ensure responsive user experience.
  • Lead requirements gathering sessions and translate ambiguous stakeholder needs into precise reporting specifications and acceptance criteria.
  • Produce month-end and quarter-end performance close reports, reconciliations, and narrative summaries for leadership review.
  • Maintain documentation of data definitions, metric catalogs, report runbooks, and dashboard design standards to enable self-serve analytics and governance.
  • Partner with data engineering to prioritize data ingestion, schema changes, and pipeline fixes that enable new reporting capabilities or improve accuracy.
  • Conduct deep-dive root cause investigations for sudden metric shifts, providing timeline-based analyses, logs of changes, and recommended mitigation steps.
  • Support forecasting and predictive modeling efforts by preparing feature sets, validating model inputs, and operationalizing model outputs into reports and dashboards.
  • Ensure analytics and reporting adhere to data governance, security, and privacy policies (RBAC, PII handling, GDPR/CCPA considerations) in collaboration with compliance teams.
  • Train business users and stakeholder groups on dashboard usage, self-service reporting best practices, and how to interpret key metrics to drive adoption.
  • Lead small analytics projects or cross-functional pods, coordinating deliverables, timelines, and communication between analytics, product, and engineering.
  • Maintain backlog of reporting requests, triage priorities with stakeholders, estimate delivery effort, and communicate timelines and trade-offs.
  • Create and deliver executive-level presentations that summarize insights, recommended actions, and measurable business impact from analytics work.
  • Continuously evaluate and recommend BI tool enhancements, plugins, or new platforms to improve reporting capabilities, scalability, and user experience.

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.
  • Mentor junior analysts by reviewing work products, sharing best practices for modeling and visualization, and conducting knowledge-sharing sessions.
  • Assist in vendor evaluations and ROI assessments for BI, data integration, and analytics tools.
  • Implement and maintain data lineage documentation to improve traceability and auditability of metrics.
  • Help create data onboarding guides and templates for new data sources to accelerate integration.
  • Monitor industry analytics trends and recommend process or tool changes to keep reporting modern and efficient.
  • Facilitate cross-functional data workshops to align on metric definitions and reporting priorities.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL skills: complex joins, window functions, CTEs, query optimization, and performance tuning for analytic workloads.
  • Expertise in BI and visualization tools: Power BI, Tableau, Looker, or equivalent; experience building governed dashboards and paginated/operational reports.
  • Data modeling and dimensional design: star schemas, slowly changing dimensions, fact table design, and semantic layers for self-serve analytics.
  • Experience with modern data warehouses and cloud platforms: Snowflake, BigQuery, Redshift, Azure Synapse, or equivalent.
  • Familiarity with ETL/ELT processes and tools: Airflow, dbt, Azure Data Factory, Fivetran, Stitch, or custom pipelines.
  • Scripting and automation: Python or R for data transformation, automation, and lightweight analytics; comfort with pandas or similar libraries.
  • Strong Excel skills including Power Query, Power Pivot, and advanced formulas for reconciliation and ad-hoc analysis.
  • Analytical methods: A/B testing, cohort analysis, hypothesis testing, regression basics, and forecasting techniques.
  • Metric governance and documentation: creating metric catalogs, data dictionaries, and runbooks.
  • Knowledge of data governance, security controls, and privacy requirements (PII handling, role-based access).
  • Familiarity with APIs and data ingestion from SaaS platforms (Salesforce, Google Analytics, HubSpot) for reporting purposes.
  • Version control and collaboration tools: Git, JIRA, Confluence, or similar platforms for reproducible analytics.
  • Optional but valuable: experience with statistical modeling or machine learning basics, DAX/MDX, and knowledge of streaming or real-time analytics architectures.

Soft Skills

  • Strong stakeholder management: extracting requirements, managing expectations, and influencing without direct authority.
  • Excellent written and verbal communication: translating technical findings into business-ready narratives and slide decks.
  • Critical thinking and problem-solving: structured approach to ambiguous metrics and the ability to break down complex issues.
  • Attention to detail and a bias for data accuracy, reconciliation, and repeatability.
  • Project management and prioritization: managing concurrent requests, estimating effort, and delivering on time.
  • Business acumen: understanding business drivers, P&L impacts, and the ability to recommend high-impact actions.
  • Collaborative mindset: working effectively across product, engineering, finance, and operations teams.
  • Teaching and enablement: ability to train end-users and create documentation to scale analytics adoption.
  • Adaptability and continuous learning: keeping pace with evolving tools, processes, and data legislation.
  • Presentation and storytelling: crafting insights that drive decisions and organizational alignment.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Bachelor’s or Master’s degree in Analytics, Data Science, Statistics, Economics, Computer Science, or an MBA with quantitative emphasis.
  • Relevant certifications (e.g., Microsoft Certified: Data Analyst Associate, Tableau Desktop Specialist, dbt Fundamentals, Snowflake SnowPro) are a plus.

Relevant Fields of Study:

  • Business Analytics / Data Science
  • Statistics / Applied Mathematics
  • Computer Science / Information Systems
  • Economics / Finance
  • Operations Research

Experience Requirements

Typical Experience Range: 2–5 years of hands-on analytics or reporting experience in a data-driven environment.

Preferred:

  • 4+ years of experience building dashboards, managing reporting pipelines, and working with cloud data warehouses in mid-size to large organizations.
  • Demonstrated experience delivering cross-functional analytics projects, defining and operationalizing KPIs, and presenting results to senior leadership.
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