Senior Regional Data

Roles & Responsibilities

POSITION SUMMARY:

Our Regional Business Intelligence Team is seeking a highly skilled Senior Regional Data & AI Engineer to join our Regional Office, based in Singapore. In this pivotal role, you will be instrumental in driving commercial growth and operational excellence across the APAC region by building a resilient data foundation. Working closely with the Corporate team and reporting to the Associate Regional Director, Data & Analytics, you will independently design, develop, and implement advanced data pipelines, data models, and AI algorithms. Your responsibilities are to transform raw data from sales, marketing, supply chain, finance, and customer interactions into actionable insights that directly contribute to business growth, efficiency, and an elevated customer experience. If you thrive on challenging data consolidation projects and want to see your work directly impact regional business success, we encourage you to apply.

KEY RESPONSIBILITIES:

• Design and Maintain Data Pipelines: Develop and maintain efficient, scalable data pipelines to extract, transform, and load data from various sources (e.g., ERP, CRM, third-party platforms) into a data warehouse.
• Ensure Data Reliability: Take end-to-end ownership of data solutions, including monitoring, troubleshooting, and performing root cause analysis to guarantee data reliability and uptime.
• Consolidate Data Sources: Identify opportunities to consolidate data sources and reduce redundant data subscriptions, with a focus on omnichannel engagement platforms.
• Manage Master Data: Create and maintain regional master data for products, customers, suppliers, and inventory to ensure consistency and accuracy for commercial analysis.
• Develop Data Models: Build and implement data models to support key commercial initiatives like sales forecasting, customer segmentation, and pricing optimization.
• Build Data Infrastructure: Construct and maintain a scalable and secure data infrastructure for both cloud and on-premise environments to handle growing data volumes.
• Collaborate on Business Requirements: Work with commercial teams to identify data-driven opportunities and translate business requirements into actionable data solutions.
• Identify and Prioritize Data Sources: Discover and prioritize new data sources within the region to enhance commercial intelligence and support business growth.
• Conduct Data Analysis: Perform exploratory data analysis to identify patterns and trends that can drive revenue growth and improve profitability.
• Ensure Data Quality and Integrity: Implement rigorous data validation, cleansing, and continuous monitoring to ensure data quality and accuracy for reporting and analytics.
• Deploy AI Models: Design, develop, and deploy AI models into production on-premise environments, while addressing data privacy and security concerns.
• Manage Data Governance: Handle data governance and compliance requirements for both cloud and on-premise data environments.

KEY REQUIREMENTS:

• At least three years of Data Engineering experience is required, with a proven track record in data ingestion, transformation, and modeling across hybrid cloud and on-premise environments.
• Strong skills in SQL and programming languages like Python or R are essential for data manipulation and model development.
• Expertise is needed in both cloud-based platforms (AWS, GCP, Azure) and on-premise data infrastructure.
• A meticulous commitment to data accuracy and code quality is required, with a history of implementing robust validation and testing procedures.
• Strong analytical and diagnostic skills to proactively identify root causes and implement sustainable solutions.
• Ability to effectively manage multiple, diverse data projects simultaneously and deliver results in a dynamic environment is a must.
• Exceptional verbal and written communication skills are necessary to translate complex technical concepts and data insights for non-technical stakeholders.
• Proven experience in creating clear, comprehensive, and maintainable technical documentation, such as data dictionaries and architecture diagrams.
• Ability to process datasets in Mandarin and work closely with Chinese markets.
• Experience with on-premise data management and security best practices is needed, and knowledge of the pharmaceutical industry is a plus.

Tell employers what skills you have

Pharmaceutical Industry
Technical Documentation
Operational Excellence
Data Analysis
Pipelines
Consolidation
Segmentation
Root Cause Analysis
Data Quality
Data Governance
Reliability
Data Engineering
Revenue Growth
GCP
Business Requirements

---