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Key Responsibilities and Required Skills for BIDW Business Analyst

💰 $ - $

Business IntelligenceData WarehouseAnalyticsBI DeveloperData Analyst

🎯 Role Definition

The BIDW Business Analyst (Business Intelligence & Data Warehouse) is a cross-functional analytics leader who partners with product owners, data engineering, analytics, and business stakeholders to design, prioritize, and deliver end-to-end BI and data warehouse solutions. This role collects and translates business requirements into technical specifications, designs dimensional data models and ETL/ELT mappings, validates data integrity, drives dashboard and reporting development, and ensures production stability and governance of analytical assets. The ideal candidate combines strong SQL and data modeling skills with practical experience in modern cloud data platforms and BI tools, plus excellent stakeholder communication and project management abilities.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Business Analyst (reporting/analytics focus)
  • Data Analyst or Reporting Analyst
  • BI Report Developer / Junior ETL Developer

Advancement To:

  • Senior BIDW Business Analyst
  • BI Product Owner / Analytics Product Manager
  • Data Architect (dimensional/enterprise)
  • Analytics Engineering Lead / Manager of BI

Lateral Moves:

  • Data Engineer (ETL/ELT/engineering-heavy path)
  • Analytics Translator / Data Strategy Consultant
  • BI Developer / Dashboard Lead

Core Responsibilities

Primary Functions

  • Lead requirements gathering sessions with business stakeholders to define measurable business outcomes, KPIs, and detailed acceptance criteria for BI reports, dashboards, and data warehouse enhancements.
  • Translate functional business needs into technical specifications, including dimensional models, schema definitions, ETL/ELT mapping documents, and logical/physical data dictionaries that engineers can implement.
  • Design and validate star-schema and snowflake dimensional models, slowly changing dimensions (SCD), conformed dimensions, and fact tables to ensure analytical consistency across reporting domains.
  • Write, review, and optimize complex SQL queries and stored procedures used for reporting, reconciliation, and data validation; tune queries for performance against large data volumes in Cloud DWs like Snowflake, Redshift, or BigQuery.
  • Define, design, and prioritize BI dashboards and interactive visualizations using tools such as Tableau, Power BI, Looker, or Qlik; produce wireframes, mockups, and prototypes to accelerate stakeholder alignment.
  • Author detailed ETL/ELT specs and collaborate with data engineers to implement pipelines in tools/platforms such as Informatica, SSIS, Talend, Azure Data Factory, dbt, or Spark/Databricks.
  • Validate end-to-end data lineage and reconciliations by developing test plans, sample datasets, and automated or manual validation queries to ensure accuracy and completeness of production reports.
  • Lead user acceptance testing (UAT) with business users, document defects, manage fix verification, and coordinate release schedules to achieve timely deployment of BI assets.
  • Manage the analytics backlog, prioritize enhancement requests using business value and technical complexity, and facilitate sprint planning and Agile ceremonies alongside product owners and engineering teams.
  • Establish and enforce data governance standards for naming conventions, metadata capture, metric definitions, and single-source-of-truth metrics to reduce ambiguity and trust issues.
  • Create and maintain comprehensive documentation: ETL mappings, data models, business glossaries, report specs, runbooks, and training materials for handoff and ongoing support.
  • Implement monitoring and alerting for critical ETL/ELT jobs and production dashboards; participate in incident response to investigate data anomalies or report failures.
  • Drive metric standardization initiatives by defining canonical metrics, opposing duplicate or conflicting KPIs, and coordinating cross-functional sign-off on business definitions.
  • Conduct root-cause analysis on data quality issues and partner with data engineering and source system owners to remediate upstream data problems or implement guardrails.
  • Build automated or scheduled reconciliations and sanity-check reports to proactively detect pipeline regressions or drift in key metrics.
  • Provide hands-on dashboard development and ad-hoc reporting support when required; create templates and reusable components to accelerate report delivery.
  • Conduct stakeholder training, office hours, and documentation sessions to increase adoption and self-service analytics maturity across business teams.
  • Collaborate with security, privacy, and compliance teams to ensure sensitive data is masked, encrypted, or appropriately restricted in BI layers and adheres to regulatory controls.
  • Support data migration, consolidation, and cloud modernizations by mapping legacy schemas to target DW models and driving cutover validation plans.
  • Mentor junior analysts and BI developers through code/review sessions, modeling best practices, and by defining standards for maintainable BI assets.
  • Work closely with product managers and business owners to define success metrics for product features and measure impact through cohort analyses, experimentation data, and funnel metrics.
  • Evaluate new BI, ETL, and data catalog tools and pilot technical solutions to improve developer productivity and reporting performance.

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.
  • Maintain and update the BI roadmap, including technical debt, modernization efforts, and recurring reporting needs.
  • Drive continuous improvement initiatives to automate manual reconciliations, reduce report latency, and improve query efficiency.
  • Liaise with third-party vendors and consultants supporting the BI/DW ecosystem to ensure deliverables meet business requirements.
  • Identify opportunities for self-service analytics and define guardrails, templates, and curated datasets to empower analysts.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert-level SQL (including window functions, CTEs, query optimization, indexing and explain plans) and hands-on experience executing and validating complex analytical queries.
  • Strong dimensional data modeling skills (star schema design, fact and dimension modeling, SCD handling, grain definition, conformed dimensions).
  • Deep familiarity with ETL/ELT processes and tools (dbt, Informatica, SSIS, Talend, Azure Data Factory, AWS Glue) and ability to write ETL specs and validate pipelines.
  • Experience with cloud data platforms such as Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, or Databricks, including performance tuning and cost optimization.
  • Proficient with BI and visualization tools: Tableau, Power BI, Looker, Qlik, or MicroStrategy — including dashboard design best practices and interactivity.
  • Practical knowledge of data orchestration and scheduling tools (Airflow, Control-M, Azure Data Factory) and monitoring frameworks.
  • Hands-on experience with data cataloging, metadata management, and data lineage tools (Collibra, Alation, Amundsen) to support governance and discoverability.
  • Familiarity with programming/scripting for analytics automation and validation: Python, PySpark, or R for data preparation, testing, or small-scale transformations.
  • Experience with source system integrations (OLTP, APIs, event streams) and understanding of upstream system limitations and data semantics.
  • Proven ability to create automated reconciliation frameworks and unit/integration tests for data pipelines.
  • Knowledge of data privacy, masking, encryption, and role-based access control implementations for BI artifacts.
  • Basic understanding of cloud cost and performance trade-offs, query warehouses, and storage-layer considerations for analytical workloads.
  • Version control and collaborative development experience (Git, CI/CD for analytics artifacts).
  • Familiarity with Agile methodologies, JIRA/Confluence for backlog management and documentation.

Soft Skills

  • Excellent stakeholder management skills with ability to facilitate cross-functional workshops, gain alignment, and translate ambiguity into actionable requirements.
  • Strong verbal and written communication capable of explaining technical concepts to non-technical audiences and producing clear specification documents.
  • Problem-solving mindset with analytical rigor to investigate data anomalies, identify root causes, and recommend durable solutions.
  • Prioritization and product-thinking: ability to balance quick wins, technical debt, and strategic initiatives against business value.
  • Collaboration and teamwork: proven track record working in cross-functional squads with engineers, product owners, and business SMEs.
  • Attention to detail and high quality standards for documentation, testing, and data integrity.
  • Mentorship and coaching ability to elevate junior team members and set standards for the BI practice.
  • Adaptability and learning orientation to stay current with evolving cloud BI and data engineering technologies.
  • Customer-oriented approach focused on delivering actionable insights and measurable business outcomes.
  • Time management and delivery discipline to drive workstreams to completion in an Agile environment.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Data Science, Business Analytics, Computer Science, Information Systems, or MBA with analytics emphasis.
  • Professional certifications (Tableau Desktop Certified, Power BI Certification, SnowPro, Google Cloud Data Engineer, Databricks, or relevant ETL/tool-specific certificates) are a plus.

Relevant Fields of Study:

  • Computer Science
  • Information Systems / Management Information Systems (MIS)
  • Data Science / Analytics
  • Statistics / Applied Mathematics
  • Business Analytics / Finance / Economics
  • Engineering (Industrial/Civil/Electrical with quantitative coursework)

Experience Requirements

Typical Experience Range: 3 - 8 years of progressive experience in business intelligence, data warehousing, analytics engineering, or reporting roles.

Preferred:

  • 5+ years of experience working specifically with BI/DW workloads, including hands-on ETL/ELT, data modeling, and dashboard development.
  • Demonstrated experience in at least one cloud data warehouse (Snowflake, Redshift, BigQuery, Azure Synapse) and one modern BI tool (Tableau, Power BI, Looker).
  • Experience operating within Agile/Scrum teams, managing product backlogs, and delivering incremental value.
  • Track record of delivering enterprise-grade analytics projects, including metric standardization, governance initiatives, and production support.
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