Key Responsibilities

1. Data Pipeline Development & Data Operations

• Design, build, and maintain robust ETL/ELT pipelines to support analytics, reporting, and product requirements.
• Develop, optimize, and refactor complex SQL queries across large-scale structured and semi-structured datasets.
• Improve data infrastructure performance, reliability, and scalability across data platforms (e.g., Oracle, PostgreSQL, BigQuery, Snowflake).
• Manage, enhance, and optimize the Customer Data Platform (CDP) to support new business use cases.
• Ensure data quality, lineage, and governance compliance across the data ecosystem.

2. Automation & Reporting Infrastructure

• Build automated reporting pipelines to support BI and recurring business processes.
• Enable analysts by developing reusable data models and curated data layers.
• Automate manual data workflows and support deployment of dashboards to internal and external stakeholders.

3. Data Products & Self-Service Enablement

• Develop and maintain a self-service reporting portal to empower cross-functional teams.
• Support data monetisation initiatives by delivering secure, high-accuracy, external-facing data products.

4. Cross-Functional Collaboration

• Partner closely with business analysts to translate analytical needs into scalable data models and pipelines.
• Work with IT teams to resolve data issues and optimize system performance.
• Provide technical support for deep-dive analytical requests requiring advanced querying or data transformation.

Required Qualifications

Technical Skills

• Strong proficiency in SQL with hands-on experience querying large and complex datasets.
• Practical experience with ETL/ELT tools, data warehousing, and modern data stack technologies.
• Experience with at least one scripting language (Python or R) for data processing or automation.
• Familiarity with BI tools (Power BI, Tableau, Looker) and dashboard deployment pipelines.
• Knowledge of relational databases and cloud data platforms (Oracle, PostgreSQL, BigQuery, Snowflake, Redshift).
• Understanding of data modelling methodologies (Star Schema, Data Vault, etc.) is a strong advantage.

Soft & Professional Skills

• Strong problem-solving mindset, with attention to data accuracy and process efficiency.
• Ability to work as the technical counterpart to a business-focused analyst.
• Comfortable working in a fast-moving retail and loyalty data environment.

Background & Education

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Statistics, Mathematics, or a related field.
• Minimum 3 years of experience in data engineering or similar technical data roles.
• Fluency in English and Vietnamese.