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The engineer needs to be adept in the use of data forensics, statistical tools, and techniques to be able to identify process gaps, root cause issues, and recommend optimal solutions. Knowledge and hands-on experience in quantitative analysis in the supply chain domain are expected. The ability to use statistical tools and techniques to identify process gaps, root cause issues, and drive data-driven decision-making is desirable.
- Provide analytical reporting and analytics to the operations team and external partners
- Ability to operate in a fast-paced, rapidly changing environment
- Ability to rapidly learn and adapt to business changes
- Create and maintain reports, create and manage data models, leverage data across complex hierarchies using multiple data sources
- Leverage process improvement techniques to drive improvements in data quality
- Detail-oriented and self-motivated individual able to function effectively when working
independently or in a team
- Ability to maintain poise and a positive attitude in a challenging environment
- Excellent communication and presentation skills
- Perform testing to support system implementations and upgrades
- Leverage industry best practices for data analysis and share across the operations community
- Minimum 3 years of experience in Data Engineering.
- Hands-on Spark / Python / Scala to process large-scale data.
- Working knowledge in Hadoop, Hive, Sqoop, and Big data
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- Company:
Dow Technologies - Designation:
Data Engineer - Profession:
IT / Information Technology - Industry:
Computer and IT