Job Description
- Scope, design data models, analyze datasets, and test data analytics applications based
on client business requirements - Collect and transform raw data into meaningful insights and present recommendations
to executives/senior leadership - Build reports, dashboards, and visuals using business intelligence tools such as Microsoft Power BI
- Analyze data to answer business questions using R or Python
- Conduct data literacy training covering the general usage, configuration, development,
enhancement, and support of Power BI and other Power Platform tools - Provide advice and implement strategies, proof-of-concepts, and cost-effective
solutions that follow best practices in data warehouse/data mart, data visualization/dashboard design, and business intelligence - Monitor data integrity and practice data ethics, especially in accordance with the Data Privacy Law
- Share knowledge with immediate peers and build communities that promote data
literacy and better technical practices across the organization
Job Qualification
- Bachelor’s degree in Engineering, Computer Science, Mathematics, Statistics, or another quantitative field
- 4+ years of experience in data analysis, data modeling, and dashboard development
- Proven working experience in data analysis using R or Python, as well as working familiarity with SQL and databases
- Excellent report/dashboard development skills using Microsoft Power BI and a good understanding of data storytelling and data visualization best practices
- Ability to scope business requirements, convert them into technical solutions and deliver software incremental
- Experience collecting data from cloud data sources such as Azure SQL DWH, AWS S3, etc.
- Experience in building data warehouses, writing complex SQL queries, schema design, and dimensional data modeling
- Experience in working with technical and non-technical teams (e.g., product, engineering, sales) and successfully providing data-related solutions/recommendations
- A good history of wrangling, processing, and extracting value from large, disconnected, structured, or unstructured data sets
