Tuyển dụng (Contractor) Data Analytics Engineer tại Hồ Chí Minh

About the Role

We are looking for a Middle Analytics Engineer (3+ years of exp) to bridge the gap between raw data infrastructure and business insights.

Your mission is not to rebuild from scratch, but to optimize, maintain, and elevate our multi-layer data warehouse to drive the digital transformation of SCV. Working closely with the Head of Digital & EC / BI Lead, you will implement data infrastructure across Digital, CRM & Ecommerce to deliver key business objectives and sales KPIs.

You will approach problems within a strategic framework and help SCV become a digital-driven as well as a data-driven organization

Please kindly note that the employment contract for this position will be signed and administered through Shiseido’s designated third-party workforce provider

Key Responsibilities

• Data Warehouse Management & Optimization: Maintain, monitor, and optimize existing data warehouse layers within Microsoft SQL Server (Warehouse Staging SDW, Warehouse Enterprise SDW, and Data Marts). Ensure data governance, data quality, and schema consistency.

• Pipeline Maintenance & Expansion: Maintain robust ETL/ELT pipelines orchestrated by Apache Airflow on a Linux VPS Cloud environment. Handle data ingestion from diverse sources: Web APIs (Shopee, Lazada, TikTok Shop, Facebook), AWS S3 (CDC), Internal DBs, Brand.com, and semi-structured/unstructured formats (JSON, Flat files, Excel, and HTML Scraping). Write new pipelines using Python and T-SQL when new business requirements arise.

• End-to-End BI & Deep-Dive Analytics: Design, develop, and maintain high-impact dashboards in Power BI Desktop (using advanced DAX and Power Query), and manage deployment on Power BI Service (including workspaces, Row-Level Security, and refresh schedules). Conduct deep-dive business analyses covering both Offline and Ecommerce sales to provide strategic insights into Seasonality, Buyer Segmentation, Assortment Growth, Channel Traffic, and Advertising & Promotion (A&P) cost efficiency.

• AI/Automation Integration: Actively apply GenAI and automation tools (e.g., GitHub Copilot, ChatGPT, LLM-based agents) to speed up code writing, document metadata, automate repetitive reporting tasks, and optimize query performance.