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Key Responsibilities and Required Skills for BI & Reporting Analyst

💰 $ - $

Business IntelligenceData AnalyticsReporting

🎯 Role Definition

The BI & Reporting Analyst is responsible for turning raw data into actionable insights by designing, building, and maintaining reports, dashboards, and data models that support strategic and operational decision-making. This role partners closely with stakeholders across finance, operations, product, and marketing to define KPIs, automate reporting workflows, ensure data integrity, and surface trends and anomalies. The ideal candidate combines strong SQL and data visualization skills with business acumen and a collaborative mindset.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst
  • Reporting Specialist / Reporting Analyst
  • Business Analyst

Advancement To:

  • Senior BI Analyst / Senior Reporting Analyst
  • BI Team Lead / Analytics Manager
  • BI Manager / Director of Business Intelligence
  • Data Architect or Analytics Engineer

Lateral Moves:

  • Data Engineer
  • Product Analyst
  • Revenue/Financial Analyst

Core Responsibilities

Primary Functions

  • Design, develop and maintain dynamic dashboards and operational reports using Power BI, Tableau, Looker or equivalent visualization tools to enable data-driven decisions across business units.
  • Translate business requirements into technical reporting specifications, including metrics definitions, aggregation logic, dimensions and filters to ensure consistency of KPIs.
  • Write, optimize and review complex SQL queries and stored procedures to extract, transform and aggregate data from relational and cloud data warehouses (e.g., Snowflake, Redshift, BigQuery).
  • Build robust semantic data models (star schema, facts and dimensions) and reusable metrics layers to support self-serve analytics and accelerate report development.
  • Implement ETL/ELT processes and pipelines—using tools such as Fivetran, Stitch, dbt, Informatica or custom scripts—to automate data ingestion, cleansing and transformation.
  • Perform thorough data validation, reconciliation and root-cause analysis to identify and remediate discrepancies between source systems and reporting outputs.
  • Define, document and socialize standard definitions for revenue, ARR, churn, conversion, pipeline and other business-critical metrics to drive consistent measurement across teams.
  • Partner with finance, product, marketing and operations stakeholders to prioritize reporting requests, scope analytics projects and deliver timely insights that influence roadmap and budget decisions.
  • Create and maintain scheduled operational reports, executive dashboards and ad-hoc analyses while ensuring SLAs for refresh cadence, performance and availability are met.
  • Monitor dashboard performance, optimize queries and implement caching or incremental refresh strategies to improve user experience and reduce compute costs.
  • Develop and maintain change control and versioning practices for report artifacts, data models and ETL jobs to ensure reproducibility and auditability.
  • Lead the design and implementation of embedded analytics and white-labeled reports for customers or internal portals, ensuring secure row-level access and data governance controls.
  • Conduct A/B test analyses, anomaly detection and cohort studies to measure feature impact, campaign effectiveness and user behavior trends.
  • Create and maintain metadata, data dictionaries and documentation for datasets, metrics and transformation logic to enable knowledge transfer and reduce tribal knowledge.
  • Support data governance initiatives by implementing access controls, classification, lineage tracking and compliance with privacy policies such as GDPR and CCPA.
  • Train and mentor business users in self-service analytics best practices, providing templates, guidelines and office hours to increase adoption and reduce ad-hoc requests.
  • Collaborate with data engineering and IT to scope and estimate deliverables, define SLAs for data availability, and prioritize infrastructure improvements that impact reporting reliability.
  • Design and implement alerting and monitoring for failed ETL jobs, missing data, or KPI thresholds to ensure timely incident response and business continuity.
  • Conduct regression testing and QA on new reports and model changes, creating automated test suites where possible to validate transformations and metric calculations.
  • Drive continuous improvement by proposing new reporting standards, visual design patterns, and automation opportunities to increase efficiency and insight quality.
  • Translate analytical findings into clear, concise presentations and storytelling with actionable recommendations tailored to executive and operational audiences.
  • Manage multiple, competing reporting projects and stakeholder expectations, documenting requirements, timelines and delivery scope with transparent communication.
  • Evaluate and recommend new BI tools, visualization libraries, and data platform enhancements that align with company scale, cost and security requirements.

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: complex joins, window functions, CTEs, performance tuning and query optimization.
  • BI & Visualization: hands-on experience building production dashboards in Power BI, Tableau, Looker, or Qlik.
  • Data Modeling: star/snowflake schemas, dimensional modeling, and metrics layer design (dbt or semantic layers).
  • Cloud Data Warehouses: experience with Snowflake, BigQuery, Amazon Redshift or Microsoft Synapse.
  • ETL/ELT Tools & Orchestration: working knowledge of dbt, Fivetran, Airflow, Matillion, Informatica or equivalent.
  • Scripting & Analytical Tools: SQL-first development plus Python or R for complex analytics, data cleansing and automation.
  • Spreadsheet Mastery: advanced Excel skills including pivot tables, Power Query and VBA or Office Scripts for rapid analyses.
  • Metric Governance & Documentation: creating data dictionaries, lineage documentation, and version-controlled metric definitions.
  • Data Quality & Testing: approaches for automated tests, reconciliation processes and anomaly detection.
  • Security & Access Control: row-level security, role-based access, and compliance with data privacy regulations.
  • Performance & Cost Optimization: techniques for optimizing queries, caching strategies, incremental refresh, and cost-control in cloud environments.
  • Familiarity with APIs and embedded analytics for integrating reports into product or customer portals.

Soft Skills

  • Strong business acumen with the ability to translate strategic questions into measurable metrics and analytics plans.
  • Excellent stakeholder management and communication; able to present complex analytical findings to non-technical audiences.
  • Problem-solving mindset and attention to detail, particularly for data quality, reconciliation and root-cause analysis.
  • Project management discipline to handle competing priorities, deadlines and cross-functional delivery.
  • Collaborative team player who partners with data engineering, product and business teams to deliver high-impact analytics.
  • Proactive ownership and initiative to identify gaps, propose solutions and drive continuous improvement.
  • Teaching and coaching capability to enable self-service analytics adoption among business users.
  • Adaptability and comfort working in fast-paced, ambiguous environments with evolving data requirements.
  • Critical thinking for hypothesis-driven analysis and A/B testing interpretation.
  • Ethical judgment with respect for data privacy, confidentiality and governance principles.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Analytics, Statistics, Economics, Finance, or related field.

Preferred Education:

  • Master's degree in Data Science, Business Analytics, Information Systems, Statistics, or MBA with analytics concentration.
  • Certifications such as Microsoft Certified: Data Analyst Associate (Power BI), Tableau Desktop Specialist/Certified, dbt Fundamentals, or Snowflake Certifications are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 2–5 years for mid-level BI & Reporting Analyst roles; 0–2 years for junior roles; 5+ years for senior or lead positions.

Preferred:

  • Demonstrated experience delivering end-to-end BI solutions: requirements gathering, data modeling, ETL/ELT, dashboard delivery and stakeholder enablement.
  • Past work in SaaS, finance, e-commerce, retail, healthcare or operations-heavy environments preferred for domain familiarity.
  • Track record of improving reporting performance, implementing data governance and enabling self-service analytics at scale.
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