Key Responsibilities and Required Skills for Business Intelligence Architect

🎯 Role Definition

The Business Intelligence Architect (BI Architect) designs, builds, and governs enterprise BI solutions and data platforms that deliver reliable, high-performance analytics. This role combines data warehouse architecture, advanced data modeling, ETL/ELT pipeline design, and BI tool leadership (Power BI, Tableau, Looker, Qlik) to provide actionable, self-service reporting and analytics for business stakeholders. The BI Architect establishes standards, leads cross-functional initiatives, and ensures scalability, security, and performance of reporting and analytics across cloud and on-premise environments.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior BI Developer or Lead BI Developer with hands-on experience designing dashboards and ETL pipelines.
Data Engineer or Senior Data Engineer focused on data warehousing and ETL/ELT best practices.
Analytics Manager or Senior Data Analyst with experience in enterprise reporting and stakeholder engagement.

Advancement To:

Director of Analytics / Director of Business Intelligence
Head of Data / Head of Analytics
Chief Data Officer (CDO) or VP of Data & Analytics

Lateral Moves:

Data Architect (Enterprise / Solution Architect focus)
Cloud Data Platform Architect (Snowflake, AWS, Azure specializations)
Analytics Product Manager / BI Product Owner

Core Responsibilities

Primary Functions

Lead the design and implementation of enterprise data warehouse and analytics architecture, including logical and physical data models, to support self-service BI, advanced analytics, and reporting at scale.
Define and enforce BI architecture standards, governance policies, metadata management, and data lineage practices to ensure consistent, auditable, and trusted analytics across the organization.
Architect and optimize end-to-end ETL/ELT pipelines using modern data integration tools and frameworks (e.g., Informatica, Talend, Azure Data Factory, AWS Glue, dbt), ensuring data quality, consistency, and performance.
Design dimensional models (star/snowflake schemas), canonical data models, and semantic layers to enable performant queries and intuitive reporting for business intelligence tools.
Lead selection, deployment, and administration of BI platforms (Power BI, Tableau, Looker, Qlik) and define best practices for dashboard design, data visualization, and user adoption.
Collaborate with data engineers, data scientists, and platform teams to design cloud-native analytics solutions using Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, or similar data warehouses.
Implement data security, access controls, row-level security, and compliance measures across BI artifacts and data platforms in coordination with security and privacy teams.
Create and maintain performance tuning strategies for SQL queries, data models, and BI extracts to reduce latency and improve report throughput for business users.
Partner with business stakeholders to gather requirements, translate business questions into analytics solutions, prioritize use cases, and design KPI frameworks and metrics definitions.
Lead proof-of-concept (POC) initiatives for new BI technologies, visualization tools, or architectural patterns and provide recommendations for enterprise adoption.
Establish a robust CI/CD process for analytics artifacts, including version control for reports, dashboards, data models, and transformation code using Git, Azure DevOps, or Jenkins.
Mentor and coach BI developers, analysts, and engineers on architecture principles, modeling techniques, ETL best practices, and BI tool capabilities to raise team competency.
Design and implement a semantic layer or enterprise data catalog to centralize business logic, standardized metrics, and canonical dimension definitions for consistent analytics consumption.
Drive data cataloging, master data management (MDM) efforts, and single source of truth initiatives to reduce duplication and discrepancies in reporting.
Conduct architecture reviews, code reviews, and design sessions to ensure scalability, maintainability, and cost-effective cloud usage for analytics workloads.
Create comprehensive documentation of BI architecture, data flows, transformation logic, data dictionaries, and runbooks for operational support and onboarding.
Manage vendor relationships and evaluate third-party BI and analytics solutions, ensuring alignment with enterprise architecture, licensing, and total cost of ownership.
Lead cross-functional projects to integrate disparate data sources (ERP, CRM, web analytics, SaaS apps) into unified reporting models and provide consolidated business insights.
Define and monitor data quality metrics, implement automated validation checks, and remediate data issues in collaboration with data stewardship and engineering teams.
Drive analytics adoption through training programs, governance of self-service BI, and establishing center of excellence practices for dashboard development and distribution.
Provide on-call support guidance and incident management leadership for production BI systems, ensuring timely resolution and minimal business impact.
Translate complex technical considerations into business-friendly roadmaps and presentations for executive leadership to secure investment in analytics initiatives.

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 evaluations, contract reviews, and proof-of-concept testing for BI and data platform tools.
Support data privacy and regulatory initiatives (e.g., GDPR, CCPA) by implementing masking, anonymization, and consent-aware reporting where applicable.
Help define SLAs and monitoring for ETL jobs, data pipelines, and BI service health metrics.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL expertise for building complex queries, window functions, CTEs, and query performance optimization across large datasets.
Data warehouse architecture and design experience (Kimball and/or Inmon methodologies), including dimensional modeling and star schema design.
Hands-on experience with cloud data platforms: Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse Analytics.
ETL/ELT tool proficiency (e.g., Informatica, Talend, dbt, Azure Data Factory, AWS Glue) and strong data transformation best practices.
Experience with BI and visualization tools: Power BI (including DAX), Tableau, Looker, Qlik — including dashboard design, embedding, and governance.
Programming/scripting skills in Python or Scala for data transformation, orchestration, and analytics automation.
Familiarity with data modeling tools (ERwin, Lucidchart, SQL Power Architect) and metadata management / data catalog tools (Alation, Collibra).
Expertise in building semantic layers, metrics layers, and business-friendly data marts for self-service analytics.
Knowledge of data security, IAM, role-based access control, and implementing row-level security within BI platforms.
Experience with source control, CI/CD pipelines, and deployment automation for analytics artifacts using Git, Azure DevOps, or Jenkins.
Performance tuning and optimization techniques for large-scale reporting workloads and OLAP-style queries.
Understanding of REST APIs and integration patterns for embedding analytics or integrating BI with operational systems.
Familiarity with containerization and orchestration (Docker, Kubernetes) for deploying analytics microservices is a plus.
Experience implementing data quality frameworks, testing strategies, and monitoring solutions for pipelines and reports.
Cloud cost optimization knowledge related to analytics workloads (query optimization, storage lifecycle, compute sizing).

Soft Skills

Excellent stakeholder management and the ability to translate complex technical concepts into clear business terms for executives and non-technical users.
Strong leadership and team development skills; experience leading cross-functional teams and mentoring junior engineers and analysts.
Strategic thinker with the ability to develop roadmaps, prioritize initiatives, and align analytics work to business outcomes and KPIs.
Exceptional communication skills—written and verbal—to produce clear documentation, architecture diagrams, and executive presentations.
Problem-solving mindset with attention to detail and a data-driven approach to decision making.
Collaborative, facilitation skills for workshops, requirements gathering, and cross-functional design sessions.
Adaptability and continual learning orientation to evaluate and adopt new technologies and patterns in BI and data engineering.
Project management and planning skills; comfortable working in Agile delivery models and contributing to sprint planning and backlog grooming.
Customer-first orientation with experience driving user adoption, training programs, and change management for BI platforms.
Ethical awareness and commitment to data privacy, compliance, and secure handling of sensitive information.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

Master's degree in Data Science, Information Systems, Business Analytics, MBA, or related advanced degree.
Relevant certifications (e.g., Microsoft Certified: Data Analyst Associate, Tableau Certified Professional, Snowflake SnowPro, AWS/Azure Data Engineer certifications).

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 7–12+ years of progressive experience in BI, data warehousing, analytics engineering, or related roles, with at least 3–5 years in an architecture or technical leadership capacity.

Preferred:

Demonstrated experience designing enterprise-scale data warehouses and BI platforms for large organizations or complex product lines.
Proven track record of leading cross-functional analytics initiatives, delivering measurable business outcomes (revenue growth, cost savings, KPI improvements).
Experience operating in cloud-native environments and migrating on-premise BI workloads to cloud platforms.
Past involvement in establishing BI Centers of Excellence, governance frameworks, and standardized metric definitions across multiple business units.