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Key Responsibilities and Required Skills for BI Analyst

💰 $70,000 - $120,000

Business IntelligenceData AnalyticsReportingBI Engineering

🎯 Role Definition

The BI Analyst is a cross-functional role that partners with business stakeholders, data engineers, and product teams to define key performance indicators (KPIs), build and maintain dashboards and reporting pipelines, and deliver analytical insights that drive strategic and operational decision-making. The ideal candidate blends strong technical capabilities (SQL, ETL, data modeling, BI tools) with business acumen and excellent data storytelling to influence outcomes.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst with strong reporting and SQL experience
  • Reporting Analyst / Business Reporting Specialist
  • Junior BI Developer or SQL Developer

Advancement To:

  • Senior BI Analyst / Lead BI Analyst
  • BI Manager or Analytics Manager
  • Data Analytics Manager or Head of Business Intelligence
  • Data Architect or Analytics Engineer (with specialized technical growth)

Lateral Moves:

  • Data Engineer / Analytics Engineer
  • Product Analyst or Growth Analyst
  • Data Scientist (with additional machine learning skills)

Core Responsibilities

Primary Functions

  • Lead the design, development, and maintenance of enterprise-grade dashboards and interactive reports in Power BI, Tableau, Looker or similar BI platforms to provide timely insights to stakeholders across finance, sales, marketing, and operations.
  • Translate complex business requirements and ambiguous questions into well-defined analytics specifications, wireframes, and technical development tasks for dashboards and reporting pipelines.
  • Write, optimize, and maintain complex SQL queries, stored procedures, and views against relational and cloud data warehouses (e.g., Snowflake, Redshift, BigQuery) to ensure performant and accurate reporting.
  • Design and maintain robust dimensional data models, star schemas, and semantic layers that support consistent KPI calculations and self-service analytics.
  • Build and maintain ETL/ELT processes and data pipelines (using tools such as dbt, Airflow, Informatica, Talend, Azure Data Factory) to ingest, transform and validate data for reporting and analytics.
  • Implement and manage a consistent metric and KPI framework, including definitions, lineage, and documentation to ensure a single source of truth across dashboards and teams.
  • Perform advanced data analysis — cohort analysis, trend analysis, segmentation, root cause analysis, and anomaly detection — to identify growth opportunities and operational issues, and present findings with clear recommendations.
  • Collaborate with product, finance, marketing, and operations stakeholders to prioritize analytics requests, align on measurement strategies (A/B testing, experiment design), and define success metrics.
  • Own data quality monitoring, validation, and reconciliation processes; investigate and remediate discrepancies between source systems and reports, and communicate impact and resolution to stakeholders.
  • Create automated scheduled reports, alerts, and data extract processes for recurring business needs and executive reporting, ensuring reliability and version control.
  • Develop reusable reporting templates, BI patterns, and self-service capabilities to reduce ad-hoc requests and scale analytics across the organization.
  • Optimize dashboard performance and query efficiency by implementing query tuning, aggregate tables, incremental loads, and caching strategies.
  • Support the migration and modernization of BI platforms and reporting stacks (on-prem to cloud, legacy to modern BI tools), participating in technical evaluation and implementation planning.
  • Document report specifications, data dictionaries, transformation logic, and dashboard user guides to enable transparency and efficient knowledge transfer.
  • Provide training, onboarding, and ad-hoc support to business users on BI tools, dashboard usage, and interpretation of metrics to increase adoption and data literacy.
  • Partner with data engineering and infrastructure teams to ensure data security, access controls, and governance policies are applied to datasets and dashboards.
  • Conduct exploratory data analysis and create visualizations that tell a compelling story, highlighting key trends, risks, and opportunities for leadership review.
  • Participate in sprint planning and agile delivery, estimating development effort, accepting user stories, and ensuring analytics deliverables meet quality and timeliness expectations.
  • Lead cross-functional analytics projects end-to-end, from requirements gathering through delivery, stakeholder acceptance, and post-deployment monitoring.
  • Mentor junior analysts, review technical code and queries, enforce best practices for data modeling and visualization, and contribute to building a high-performing analytics team.
  • Drive continuous improvement of BI processes, tooling and standards by gathering stakeholder feedback, tracking usage metrics, and implementing enhancements.

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 with preparing board-level and executive presentations that synthesize analytical findings into strategic recommendations.
  • Help evaluate new BI tools, data platforms, and visualization libraries to improve speed-to-insight and reduce maintenance overhead.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert SQL skills for querying, optimizing and validating data across OLTP and OLAP systems.
  • Hands-on experience with at least one major BI/visualization tool: Microsoft Power BI, Tableau, Looker, Qlik, or similar.
  • Data modeling and database design knowledge: star schema, snowflake, facts and dimensions, slowly changing dimensions (SCDs).
  • Practical experience with cloud data warehouses and analytics stacks: Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse.
  • Familiarity with ETL/ELT workflows and orchestration tools (dbt, Airflow, Azure Data Factory, Informatica, Talend).
  • Proficiency in a scripting language for data manipulation and automation (Python or R) including use of pandas, numpy, or tidyverse.
  • Experience writing DAX, MDX, or equivalent expression languages for calculated measures and advanced metric logic in Power BI/Analysis Services.
  • Strong knowledge of data quality practices, testing, reconciliation and data validation techniques.
  • Familiar with analytics concepts including cohort analysis, funnel analysis, segmentation, correlation, regression and experiment (A/B) analysis.
  • Familiarity with version control systems (Git) and CI/CD practices for analytics code and dashboard deployments.
  • Experience with metadata, data lineage, and documentation tools (data catalogs, Confluence, ER diagrams).
  • Understanding of data governance, security, and access control best practices for enterprise reporting.

Soft Skills

  • Exceptional data storytelling and visualization judgement; able to convert complex analysis into clear, action-oriented narratives for executives and non-technical stakeholders.
  • Strong stakeholder management and consulting mindset; able to elicit requirements, manage expectations, and prioritize trade-offs.
  • Excellent written and verbal communication skills with experience preparing executive summaries and presentation decks.
  • Critical thinking and strong problem-solving aptitude when confronting ambiguous or incomplete data scenarios.
  • Collaborative team player who partners effectively with product managers, engineers, and business owners.
  • Time management and project planning skills to balance multiple concurrent dashboard and analysis requests.
  • Attention to detail and a quality-first mentality when validating metrics and delivering production reporting.
  • Adaptability and continuous learning mindset to stay current with emerging BI tools, cloud technologies, and analytical techniques.
  • Influence and negotiation skills to align stakeholders on metric definitions and measurement approaches.
  • Coaching and mentorship ability to develop individual contributors and raise team BI capabilities.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Data Science, Business Analytics, Statistics, or MBA with strong analytics emphasis.
  • Relevant certifications such as Microsoft Certified: Data Analyst Associate, Tableau Desktop Certified, Certified Business Intelligence Professional (CBIP), or Snowflake/Cloud DW certifications.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 2–5 years of hands-on experience in BI, reporting, or analytics roles.

Preferred: 4–7+ years of progressive BI experience with demonstrated ownership of end-to-end reporting solutions, experience working with cloud data warehouses and modern BI stacks, and proven track record of influencing business outcomes with data-driven insights.

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