Data Analyst

1. Master Data & Data Quality

Clean, deduplicate and verify outlet master data using name, address, GPS and client codes.

Work with the Technology & Data Leader to define master ID standards Staff / Outlet / Project / SKU / POSM and build crosswalk tables mapping legacy codes to standard IDs. 2. Analytics, Metrics & Dashboard

Define and standardize metrics & KPIs; build interactive dashboards on Power BI / Looker / Metabase to replace static reports.

Analyze operation, field execution, retail audit and project performance data by project, region and workforce group. 3. Workforce Scoring & Field Analytics

Build scoring models for the mass/field workforce across discipline, productivity, quality and capability.

Map staff to projects, job types and regions to support allocation, evaluation and operational optimization. 4. AI-ready Data Layer

Build and maintain the semantic/metric layer for the internal AI chatbot/text-to-SQL, ensuring accurate and permission-aware answers.

Leverage AI/GenAI tools to accelerate data cleaning, entity resolution and analysis. 5. Documentation, Governance & Expected Outputs

Set up a weekly Data Health Scorecard covering freshness and completeness; propose source-level validation rules.

Write and maintain the data dictionary and data-entry SOPs, laying the foundation for the Data Warehouse and Data Moat.

Within the first 3–6 months, the role is expected to standardize key master data, build core operational dashboards and establish a recurring data health scorecard.

Other tasks as assigned by the Line Manager.

Must-have

requirements:• Bachelor's degree in Data, Statistics, Computer Science, Information Systems or a related field.

3–5 years of hands-on experience as a Data Analyst / BI Analyst / Data Engineer.

Strong SQL on large datasets: joins, window functions, CTEs; Python/pandas for cleaning, deduplication and data analysis.

Proficient in at least one BI tool: Power BI / Looker / Metabase / Tableau; advanced Excel / Google Sheets.

Data modeling mindset: master data, dimension/fact, master ID and crosswalk table design.

English: able to read/write technical documentation and build client-facing dashboards. Strong plus

Experience with fuzzy matching / record linkage: rapidfuzz, recordlinkage, Splink.

Understanding of semantic/metric layers, text-to-SQL, RAG or practical use of AI/code assistants in data work.

Basic machine learning knowledge for scoring models: regression/classification, feature engineering.

Knowledge of modern data stack: dbt, Airflow/Dagster, Git; data-quality frameworks such as Great Expectations. Preferred background

FMCG, trade marketing, field execution, retail audit, geo-data or data warehouse experience.

Awareness of personal data protection/PII and Decree 13/2023.

Proactive, disciplined in documentation, able to work independently and coordinate well with Field, Tech and client teams.

Competitive and transparent salary and bonus structure, along with full allowances in accordance with company policy (lunch, phone, 13th-month salary, etc.) 100% salary during probation, with annual salary review Statutory insurance coverage and additional private health insurance after probation Be part of a visionary company striving to be the leading service provider, delivering excellence through innovation, integrity, and compassionate leadership, while fostering employee happiness and enriching lives for all Work in a value-driven environment emphasizing integrity, compassion, and innovation.

Opportunities for career growth and professional development.

Impactful Work: Contribute to projects and initiatives that drive meaningful change for our clients and communities.