Role Overview :

As the Senior Data/BI Engineer (assistant manager) at CCBJI, you will play a crucial role in leading our Data engineers and their data management and business intelligence efforts.

This leadership position is responsible for managing and developing a team of data engineers and BI professionals, while also overseeing the design, development, and maintenance of data infrastructure and business intelligence solutions.

Your leadership will ensure correct, reliable and timely data is harnessed for informed decision-making, enabling our organization to excel in the data-driven landscape.

Stakeholders :

Master Data Project Team, ITO and operations teams, Enterprise architect, Data architects, business data owners, business data stewards, Data and analytics group, data and digital teams under functions like SCM and Commercial, BI engineers' team.

Key Responsibilities :

- Manage and lead a team of data engineers and BI professionals, setting priorities and providing guidance on best practices for visualization and performance.

- Design, develop, and maintain data pipelines and ETL processes to ensure data is efficiently extracted, transformed, and loaded into data warehouses.

- Optimize SQL queries for data retrieval and analysis while ensuring best practices.

- Build and maintain interactive dashboards and reports using business intelligence tools (e.g., Tableau, Power BI) to provide actionable insights to end-users.

- Develop industry leading UI/UX frontend solutions to provide superior user experience

- Collaborate with business stakeholders to understand analytics requirements and provide industry leading visual solutions for the same.

- Translate BI requirements into data requirements and write suitable requirementdocuments.

- Identify opportunities for improving current analytics products into visually appealing and easy to interpret analytics products.

- Ensure data security and compliance with data privacy regulations.

- Stay updated on emerging trends and technologies in data engineering and business intelligence.

Qualifications :

- Education Bachelor's degree in computer science, Information Systems, or a related field. Advanced degree preferred.

- Experience Minimum of 7-10 years in data engineering, business intelligence, or related roles, with at least 3 years in a leadership or management position.

- Proficiency in Business Intelligence products

- Strong technical knowledge of Power BI, Spotfire, Tableau and other BI tools

- Experience in data modelling (star schemas) for data marts (business/semantic layer) and reporting layers

- Experience in delivering highly performant insights and analysis on large volumes of complex datasets

- Experience in delivering visually appealing, easy to use and interpret data products

- Strong functional knowledge of KPIs used in consumer goods domain or retail domains.

- Exceptional problem-solving, communication, and leadership skills.

- Proven ability to work collaboratively with cross-functional teams.

- Fluency in English

Good to Have :

- Domain knowledge in SAP (ECC, APO, HANA, etc.) based supply chain management environments

- In-depth knowledge of data privacy and protection regulations.

- Experience with big data technologies (e.g., Hadoop, Spark).

- Familiarity with machine learning and data science concepts.

- Any kind of business domain experience or specialization in areas like sales, supply chain, finance.

- Fluency in Japanese

KPIs to build and monitor :

- Dashboard User Engagement The number of dashboard views, number of distinct users, number of user interactions and executions, and feedback received.

- Number of times for excel extraction and/or analysis in total number of executions of same dashboards.

- Dashboard usage by SLT users Number of active users vs total registered

- Query Performance Average time used for SQL Query execution, number of optimization points identified,

- Data Security Compliance number and percentage of data security incidents, number of incidents resolved in a timely manner based on general SLA rules.

- Data Model Accuracy The percentage of data model changes that result in improved data accuracy.

- Team Productivity Percentages and feedback for project completion, timely delivery, number of defects after delivery, and adherence to best practices.

- Satisfaction Qualitative feedback from team members, business counterparts, and data stewards to gauge their satisfaction with the reporting / dashboarding deliveries and leadership (score 0-5)