Key job responsibilities
1. Analyze complex data sets to identify trends, patterns, and anomalies.
2. Develop and maintain dashboards and reports to track key performance indicators (KPIs)
5. Conduct root cause analysis on inventory discrepancies and quality issues
6. Monitor data quality and partner with data owners to flag and resolve data quality issues
7. Create and maintain documentation for data processes and methodologies
8. Provide regular performance updates to stakeholders and leadership
9. Perform diagnostic and proactive analyses to identify inventory and quality trends, partnering with data scientists for advanced statistical needs
10. Stay up-to-date with industry best practices and emerging technologies in data analyticsA day in the life
Gather and translate business requirements into technical specifications and backend data logic.Design and build backend data solutions using SQL and Python for data processing and automation.Develop dashboards and reports using QTS BI tools to communicate findings to stakeholders.Continuously refine data pipelines and logic based on user feedback and evolving requirements.
- Experience creating complex SQL queries joining multiple datasets, ETL DW concepts
- Experience with reporting and Data Visualization tools such as Quick Sight / Tableau / Power BI or other BI packages
- Experience defining requirements and using data and metrics to draw business insights
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