Data Engineering Manager

1. Enterprise Data Restructuring and Standardization

• Assess and map all existing data sources across departments, including:
  • Sales and distribution, E-commerce and marketplaces, Marketing and media
  • CRM and loyalty programs, Finance and accounting
• Identify data silos, inconsistencies, redundancies, and gaps.
• Design and implement company-wide data architecture and taxonomy.
• Establish data standards, naming conventions, and data dictionaries.
• Create governance policies to ensure long-term consistency.

2. Data Categorization and Classification

• Define standardized categories and hierarchies for:
  • Product portfolios, SKUs, Customer segments, Distribution channels
  • Marketing campaigns, Promotional activities, Sales territories, Suppliers and partners
• Build metadata structures and classification frameworks.
• Maintain centralized reference tables and master datasets.
• Ensure consistent data definitions across departments.

3. Data Template and User Framework Design

• Design standardized templates and data input forms for business users.
• Develop data collection frameworks that minimize human errors.
• Create guidelines and SOPs for data entry and maintenance.
• Improve usability for non-technical teams.
• Train stakeholders on proper data handling practices.
• Establish validation rules and approval workflows.

4. Data Cleaning and Quality Management

• Lead data cleansing initiatives across all business functions.
• Identify and remove:
  • Duplicate records
  • Missing values
  • Incorrect classifications
  • Inconsistent formats
  • Outdated records
• Implement automated validation checks.
• Define KPIs for data quality, including:
  • Accuracy Completeness Timeliness Consistency Reliability

5. Data Query, Validation, and Format Consistency

• Develop and optimize SQL queries to extract and validate business data.
• Build automated rules to ensure format consistency.
• Monitor data pipelines and troubleshoot discrepancies.
• Create reusable query libraries for internal teams.
• Ensure data accuracy before executive reporting.

6. Data Integration and Business Intelligence Enablement

• Integrate data from multiple systems, including:
  • SAP B1 KISSFLOW CRM
• Support dashboard development and executive reporting.
• Enable cross-functional insights for management decision-making.
  

2. คุณสมบัติขั้นต่ำของตำแหน่งงาน (โปรดระบุ)

• Bachelor’s degree in computer science, Data Engineering, Information Systems, Statistics, Business Analytics, or a related field.
• 7–10 years of experience in data engineering, business intelligence, or related fields.
• Minimum of 2 years of experience leading a team.
• Experience working in the FMCG, cosmetics, beauty, retail, consumer goods, or e-commerce industries is preferred.
• Proven experience managing fragmented and siloed enterprise data environments.
• Experience implementing data governance and master data management (MDM) frameworks.
• Technical Skills

1. Data Engineering

· Advanced SQL expertise., ETL/ELT pipeline development, Data warehousing design.

· Data modeling, Database optimization.

2. Business Intelligence

· Power BI, Tableau, Looker, Google Data Studio.

3. Databases

· Microsoft SQL, PostgreSQL, SQL Server, BigQuery, Snowflake (preferred).

4. Data Integration

· API integration, Excel automation, Google Sheets.

Soft Skills

• Strong analytical and problem-solving skills.
• Strategic thinking with business acumen.
• Excellent communication and stakeholder management skills.
• Ability to simplify complex data concepts for non-technical users.
• Project management capabilities.
• Attention to detail and commitment to data accuracy.
• Change management and process improvement mindset.
• Strong leadership and coaching abilities

Work Location: Head Office 50 Rama9 Soi53