Data Business Intelligence (BI) Architect

The Data Business Intelligence (BI) Architect role is a hybrid of data architecture, engineering, and business strategy — bridging the gap between technical data solutions and business objectives. This individual will design, develop, and maintain the overall data strategy, ensuring that organizational data is accessible, reliable, and secure for analysis and decision-making. The ideal candidate will have experience architecting data solutions for descriptive, diagnostic, predictive, and prescriptive analytics. Key Responsibilities • Stakeholder Collaboration: Partner with business and IT stakeholders to gather requirements and translate business needs into technical specifications, including identification of data sources. • Data Architecture & Modeling: Architect and implement scalable, secure, and efficient data solutions (warehouses, lakes, marts). Design conceptual, logical, and physical data models. • Tool & Platform Selection: Evaluate, recommend, and implement tools aligned with business and technical architecture, including BI and visualization tools. • ETL/ELT Pipeline Management: Design, develop, and test data pipelines and integration processes to ensure smooth data movement from source systems into the data warehouse. • Data Catalog & Metadata Management: Create and maintain an enterprise-wide data catalog, automate metadata ingestion, and ensure all assets are documented and tagged. • Data Governance & Discovery: Enforce governance policies to ensure data quality, security, and compliance. Enable intuitive, self-service data discovery for users. • Performance Optimization: Monitor and optimize BI systems and pipelines for high performance, reliability, and cost-effectiveness. • Technical Leadership: Provide mentorship and establish best practices for data management, BI development, and analytics. Technical Environment Experience with some or all of the following technologies is beneficial (not required to have all): • Data Platforms: Data warehouse/lake concepts, dimensional modeling, and cloud services (AWS S3, Redshift, RDS, Azure Data Lake, Synapse Analytics, BigQuery, Databricks, Snowflake, Informatica). • Databases: SQL Server, Oracle, PostgreSQL, MongoDB. • BI Tools: Power BI, Tableau, Business Objects, Looker, Crystal Reports. • ETL/ELT Tools: AWS Glue, Azure Data Factory, Google Cloud Dataflow, Fivetran, Talend, dbt. • Big Data Tech: Hadoop, Spark, Kafka. • Programming & APIs: Python, R, XML, Keras, Scikit-learn. • ML/DL Engines: TensorFlow, PyTorch, Trillium, Apache Spark. • Modeling Tools: MS Visio, ER/Studio, PowerDesigner. • Source Systems: On-premise, cloud, and SaaS.