We are seeking a talented and experienced Business Intelligence Engineer (BIE) to join our team. In this role, you will be responsible for developing and implementing measurement mechanisms, data frameworks, and interactive dashboards and visualizations to drive data-driven decision-making across the organization. You will work closely with stakeholders to understand their analytical needs and provide self-service analytics and reporting capabilities.
The BIE on the SPIA Analytics Team is responsible for supporting the Benefit Measurement and Impact Calculation program for the SPS organization. They will measure initiatives implemented to improve the Selling Experience and will be instrumental in reporting on these initiatives.
Key job responsibilities
1. Measure initiatives for Seller Experience through A/B testing or non-causal analysis methods.
2. Utilize advanced SQL/Python skills for business decision-support analysis.
3. Create effective visualizations and dashboards that tell a compelling story and provide recommendations for new business initiatives.
4. Conduct driver analysis and develop time series models to forecast trends in metrics.
5. Collaborate with Product Managers and the Science team to ensure alignment between Insight and Science used.
6. Provide training and support to business users, ensuring effective adoption and utilization of BI solutions.
7. Continuously monitor data quality and optimize data flows to ensure efficient and reliable data delivery.
8. Identify, develop, manage, and execute analyses to uncover opportunities, providing written recommendations.
9. Communicate data clearly and concisely, adjusting your style for different audiences to address complex financial issues effectively. Your communication will influence critical business decisions.
10. Become an expert in your field to promote work-life balance. Be a positive, collaborative influence on new team members, exemplifying operational excellence and efficiency.
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with Experimentation and Non Experimental Causal attribution models
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
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