Regional e-Commerce Data Manager

As a Regional e-Commerce Data Manager at Samsung, your primary responsibilities will include developing and managing data systems for recurring reports, ensuring data accuracy, collaborating with cross-functional teams, and identifying process inefficiencies to implement improvements. You'll be tasked with automating data processes and enhancing workflow optimization while upholding the company's data policies.

To qualify for the Regional e-Commerce Data Manager position at Samsung, you should have a degree in Data Analytics, Computer Science, or a related field. Additionally, you will need 7-10 years of experience in data operations and automation, preferably in an e-commerce or retail setting, as well as proficiency in Python, VBA, and SQL.

In the role of Regional e-Commerce Data Manager at Samsung, you will work extensively with Python and SQL for coding, as well as utilize MS Office applications for report creation. Familiarity with GitHub and MS Visual Studio is essential, and knowledge of additional languages like R is a plus.

Samsung places a strong emphasis on data quality in the Regional e-Commerce Data Manager role. You will work with stakeholders to ensure timely and accurate data reception, develop automated systems to identify data errors, and implement processes to correct anomalies effectively, maintaining the integrity of our data systems.

Key skills for success as a Regional e-Commerce Data Manager at Samsung include strong attention to detail, proficiency in relevant programming languages, the ability to work efficiently under tight deadlines, and solid experience with large datasets. Your capability to adapt and communicate across diverse teams will also be crucial.

Certainly! Automation in data management, especially for a Regional e-Commerce Data Manager, is crucial as it streamlines processes, reduces manual errors, and allows for quicker report generation. Discuss how you've used automation tools to enhance efficiency in past roles.

In your response, provide specific examples of projects where you utilized Python and SQL for data processing tasks. Highlight any challenges you faced and how your skills helped you overcome them, demonstrating your technical expertise.

Mention that ensuring data accuracy involves several steps, including data validation techniques, routine checks, and collaboration with stakeholders to understand data needs. It's important to describe your systematic approach to maintaining data reliability.

Discuss strategies like using optimized queries, indexing, and employing data chunking when necessary. Emphasize any experience with tools that facilitate handling large data volumes, especially in a fast-paced environment.

Outline a logical process that includes data validation, systematic tracking of discrepancies, and your methods for collaborating with teams to fix issues. Providing examples from past experiences will add credibility to your response.

Explain your collaborative approach where you engage with various teams to collect requirements, followed by your method of utilizing data analysis techniques to deliver insights that align with business goals.

Discuss the importance of clear communication with stakeholders and cross-functional teams, including how you tailor data presentations to suit diverse audiences to ensure everyone's on the same page regarding data metrics and insights.

Mention subscriptions to industry-related publications, attending webinars or workshops, and participating in forums or community groups that focus on data analytics advancements as strategies you employ to stay informed.

Highlight considerations such as ensuring reliability, understanding the need for scalability, and testing your automated solutions thoroughly before full implementation to prevent initial errors that could propagate.

Share your method for prioritizing tasks based on urgency and importance, possibly mentioning tools or techniques like the Eisenhower Box or Kanban boards that you've used to keep your workload organized and manageable.