Key Responsibilities and Required Skills for BI Support Specialist

🎯 Role Definition

The BI Support Specialist is a hands-on technical and business-facing role responsible for maintaining, troubleshooting, and enhancing the organization's business intelligence portfolio. This role combines expertise in reporting platforms (Power BI, Tableau, Looker), SQL and ETL processes, and strong stakeholder communication to ensure accurate, timely insights and scalable analytics solutions. The BI Support Specialist acts as the first line of support for BI consumers, owns incident resolution, performs root cause analysis of data issues, and partners with data engineering and product teams to implement improvements and automation that increase data trust and speed to insight.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst with reporting and SQL experience
BI Analyst or Reporting Analyst transitioning to support and production ownership
Junior ETL or Analytics Engineer with hands-on dashboard exposure

Advancement To:

Senior BI Analyst / Senior BI Support Specialist
BI Manager / Analytics Manager
Data Engineering or Analytics Engineering roles (ETL/ELT lead)
Data Product Owner or Head of Analytics

Lateral Moves:

Data Product Analyst
Data Governance Specialist
Business Systems Analyst

Core Responsibilities

Primary Functions

Serve as the primary point of contact for BI incidents and service requests, triaging and resolving production reporting issues within defined SLAs while communicating status and impact to stakeholders.
Maintain, publish, and troubleshoot interactive dashboards and operational reports in Power BI, Tableau, Looker, or similar platforms, ensuring visualizations reflect accurate metrics and adhere to design and accessibility standards.
Author, optimize, and maintain complex SQL queries and views used by reports and dashboards; perform query tuning and indexing recommendations to reduce report latency and improve user experience.
Partner with data engineering and ETL teams to diagnose and remediate upstream data pipeline failures, data drift, schema changes, and ingestion errors that impact BI outputs and downstream analytics.
Execute thorough data validation and reconciliation processes against source systems and data warehouse tables to certify report accuracy before deployment to production environments.
Implement and maintain row-level security, dynamic filters, user access controls, and report-level permissions to ensure sensitive data is only accessible to authorized users in accordance with data governance policies.
Develop and maintain ETL/ELT job scripts, incremental load processes, and transformation logic (using SQL, Python, or vendor tools) to automate data refreshes and maintain data freshness for reporting consumers.
Build and document reusable semantic layers, data models, and star schemas in the data warehouse to standardize definitions of key business metrics (revenue, ARR, churn, bookings) and reduce duplication of work.
Conduct root cause analysis for recurring BI issues and produce remediation plans, ticket documentation, and playbooks that reduce repeat incidents and accelerate time-to-resolution.
Design and implement monitoring, alerting, and observability for BI environments and data pipelines (e.g., data quality checks, pipeline latency alerts, refresh failure notifications) to proactively detect and resolve issues.
Translate partner and business stakeholder requirements into prioritized tickets, acceptance criteria, and design specs for BI enhancements, ensuring alignment with KPI definitions and reporting standards.
Lead or participate in deployment and release management for BI artifacts, using version control, CI/CD processes, and environment promotion (dev → test → prod) to manage report lifecycle and governance.
Create and maintain operational and technical documentation, runbooks, data dictionaries, and onboarding materials that enable self-service reporting and reduce the support burden on the BI team.
Provide end-user training, office hours, and enablement sessions for business users, teaching them to use dashboards effectively, build custom ad-hoc reports, and interpret key metrics.
Implement and enforce best practices for visualization, storytelling, and metric governance — ensuring consistent naming conventions, KPI definitions, and dashboard usability across the organization.
Support compliance and audit requests by extracting evidence, report histories, access logs, and configuration snapshots demonstrating report lineage and data transformations.
Collaborate with product, finance, operations, and marketing teams to instrument events and tracking that feed BI systems, ensuring correct event taxonomy and high-fidelity analytics.
Automate repetitive support tasks and report generation via scripting (Python, Bash) or workflow automation tools to improve team efficiency and reduce manual intervention.
Conduct performance tuning of BI server infrastructure (query caching, extract refresh scheduling, concurrency limits) and recommend capacity planning adjustments to meet business demand.
Implement and maintain integration of BI tools with cloud data warehouses and analytics platforms (Snowflake, Redshift, BigQuery, Azure Synapse) and manage connectivity, credentials, and data transfer patterns.
Participate in cross-functional design reviews and agile sprint ceremonies to embed BI best practices early in product feature development and to prioritize high-impact analytics work.
Maintain a backlog of change requests and continuous improvement initiatives for dashboards, prioritizing items that deliver measurable business value and reduced support volume.

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 vendor evaluation and tool selection for BI and analytics platforms.
Act as a liaison between IT, security, and business teams for data access requests and compliance reviews.
Perform periodic audits of reports to retire stale artifacts and consolidate redundant dashboards.
Mentor junior analysts on report development, SQL best practices, and BI platform capabilities.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL skills for data extraction, transformation, optimization, and debugging in relational and columnar warehouses.
Hands-on experience building and supporting dashboards and reports in Power BI, Tableau, Looker, or Qlik (design, publishing, and performance tuning).
Practical knowledge of ETL/ELT concepts and tools (e.g., dbt, Informatica, Talend, Fivetran, Stitch) and experience troubleshooting pipeline failures.
Experience with cloud data warehouses and analytics platforms such as Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.
Familiarity with scripting and automation using Python, Bash, or similar languages to automate support tasks and ETL jobs.
Understanding of data modeling patterns (star schema, snowflake), dimensional modeling, and building semantic layers for consistent metrics.
Experience implementing security, role-based access control (RBAC), and row-level security in BI platforms and data warehouses.
Proficiency with report lifecycle management, version control (Git), and CI/CD pipelines for analytics deployments.
Knowledge of data quality frameworks, monitoring tools, and implementing data validation checks across pipelines.
Ability to troubleshoot API integrations, data ingestion connectors, and authentication flows between systems.
Familiarity with DAX, MDX, or advanced platform-specific expression languages used in Power BI or other BI tools.
Experience with performance monitoring and tuning of BI servers, caching strategies, and extract refresh scheduling.
Basic knowledge of cloud platforms (AWS, Azure, GCP) for BI infrastructure and managed analytics services.

Soft Skills

Strong stakeholder management and communication skills: able to translate technical findings into business impact and present clear recommendations.
Customer service orientation with a sense of urgency and professionalism when resolving production issues.
Analytical problem-solving mindset with attention to detail and a methodical approach to root cause analysis.
Ability to prioritize competing requests and manage time across support, enhancement, and maintenance tasks.
Collaborative team player who can work cross-functionally with data engineers, product managers, and business leaders.
Documentation-oriented: generates clear runbooks, SOPs, and training materials to scale support processes.
Adaptability and continuous learning mindset to stay current with BI tool updates, cloud services, and analytics best practices.
Strong project management instincts for coordinating releases, cutovers, and stakeholder communications.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Systems, Data Analytics, Business Administration with analytics focus, or a related technical field.

Preferred Education:

Master's degree in Analytics, Data Science, Business Intelligence, or MBA with analytics emphasis.
Professional certifications (e.g., Microsoft Certified: Data Analyst Associate, Tableau Desktop Specialist/Certified Professional, Google Cloud Professional Data Engineer, Snowflake Certified).

Relevant Fields of Study:

Computer Science
Information Systems
Data Analytics / Business Analytics
Mathematics / Statistics
Business Intelligence / Data Engineering

Experience Requirements

Typical Experience Range: 2 - 5 years working in BI, analytics, reporting support, or a related role.

Preferred: 3+ years of direct experience supporting enterprise BI platforms and data warehouses, demonstrable track record of managing production incidents, developing dashboards in at least one major BI tool (Power BI, Tableau, or Looker), and strong SQL competency.