WHAT DOES A BI ENGINEER DO?
The Business Intelligence Engineer collaborates with teams to design and maintain analytical solutions, optimizing data architecture and visualization to support organizational initiatives. This role involves identifying data trends and correlations through robust analysis and developing actionable insights for both technical and non-technical executive leaders. Additionally, the engineer continuously enhances data collection and reporting automation by integrating new data sources and building self-service tools in a cloud-based environment.
A Review of Professional Skills and Functions for BI Engineer
1. BI Engineer Duties
- Cross-Department Collaboration: Works with various members of the Labs team and key members of other departments.
- Data-Driven Decision Making: Enables the adoption of data-driven decision-making across the different functional areas of the Labs team.
- Business Intelligence Identification: Collaborates with key members of the Labs team to identify their business intelligence needs.
- BI Solution Implementation: Implements solutions that address lab teams' business intelligence needs.
- BI Tool Utilization: Utilizes Power BI and other BI tools, ensuring accuracy and reliability of reports and underlying data.
- Reporting and Dashboard Creation: Creates reports and dashboards ensuring timely access to information for all relevant parties within the Labs team.
- Process Documentation: Assists in documenting data analytics processes and high-level data flow charts.
- Data Documentation: Documents data sources at a report level, in addition to any cleansing, filtering, or transformations applied to raw data.
- Data Administration Support: Assists in data administration, modeling, and integration activities in data warehouse systems.
- Business Glossary Production: Assists in producing a business glossary for critical data, defining and explaining data from the business perspective.
2. BI Engineer Details
- Business Understanding: Builds an understanding of the business to explore and analyze trends within new and existing data sources.
- Profitability Optimization: Identifies opportunities for increased profitability and efficiency within the organization.
- Technology Evaluation: Evaluates emerging data analytics technologies and business intelligence tools.
- Thought Leadership: Responsible for providing thought leadership and suggestions for adoption.
- Data Governance: Performs data steward responsibilities for Labs, following the Bank’s Data Governance Program.
- Decision Making: Regularly exercises discretion and judgment in the performance of essential job functions.
- Professional Conduct: Maintains good punctuality and attendance at work, and follows Bank policy, procedures, and guidelines.
- Data Optimization: Performs data evaluations on large sets of company data to optimize marketing, management, and sales activities.
- Data Presentation: Creates online and offline dashboards and data extracts using modern data presentation technologies.
- Knowledge Sharing: Knows the data warehouse, shares knowledge and provides support and advice to internal customers.
- BI-Platform Development: Takes part in development activities related to the internal BI-Platform.
3. BI Engineer Responsibilities
- Data Collection Development: Build Data Collection platforms/systems that turn work done in the field into useful metrics.
- Predictive Analytics Creation: Build systems that use AI with Machine Learning to give us predictive analytics.
- Financial Data Modeling: Transform data from the accounting team into models that allow us to predict the future and adjust accordingly.
- Algorithm Implementation: Jointly with Machine Learning Engineers, implement algorithms in large-scale distributed environments.
- Software Development: Write and maintain SW code for different projects in Big Data environments.
- Coding Practice Mentorship: Support and mentor Data Scientists to apply good SW coding practices while writing code.
- Software Management: Responsible for the code base and putting the SW code into production.
- Quality Assurance: Documents the work consistently and completely, and ensures software quality.
- Business Problem Analysis: Identify and understand business problems to help the team design the right solution.
- Analytics Leadership: Responsible for leading healthcare analytics visualization projects.
4. BI Engineer Job Summary
- Design and Maintenance: Work collaboratively across the team and organization to design, build, and maintain analytical solutions to support Stars initiatives.
- Data Architecture Application: Work collaboratively with teammates to optimally apply data warehouse, cloud, BI architecture, and visualization techniques.
- Data Retrieval: Identify relevant data, use high-quality code to retrieve it, and perform all relevant data transformations for proper consumption.
- Data Analysis: Analyze data to uncover trends and correlations to develop insights that can improve business, internal processes, and technology.
- Executive Communication: Responsible for articulating findings and insights with tech and non-tech executive leaders.
- Visualization Development: Conceptualize and develop automation opportunities for data collection and self-service data outputs through data visualization tools.
- Continuous Learning: Continuously learn new systems, tools, and industry best practices to help design new studies and build benchmarking products.
- Data Integration: Add new data sources to a cloud-based data warehouse using extract, transform, load (ETL) tools and custom connectors.
- SQL Development: Design and develop SQL-based views in a data warehouse to enable multi-sourced data analysis.
- Application Testing: Perform adequate unit tests for applications developed and validate expected results.
5. BI Engineer Accountabilities
- Data Structure Design: Design, develop and implement enterprise data structures.
- Data Warehouse Development: Design and develop an enterprise level Data Warehouse.
- Database Architecture: Database architecture, data mapping and data modeling.
- ETL Development: Developing and maintaining ETL processes and tools.
- BI Tool Implementation: Responsible for developing and implementing BI tools.
- Business Requirements Gathering: Work with business users to gather business requirements.
- Data Integration Coordination: Coordinate the integration of data to determine Stars' trends and impacts.
- Data Communication: Communicate insights and analytical findings to a broad range of audiences.
- Data Quality Assurance: Ensure solutions provide the highest quality of data daily.
- Cloud Platform Transition: Contribute to moving the team towards cloud-based analytical platforms.