In-House Data Engineer & Analyst (One Role) – E-commerce Optimization Focus

Description:
We’re hiring a full-stack data professional — someone who can both build the data infrastructure and analyze it to generate actionable insights. This is an in-house role (not outsourced or project-based) for someone who can grow with us long-term.

We run a fast-scaling e-commerce business across Southeast Asia and need someone obsessed with clean data, clarity, and impact.

What You’ll Do:
• Set up and maintain data pipelines across Shopify, Shopee, Lazada, TikTok, and internal tools
• Organize and clean messy operational, sales, ad, and logistics data
• Build dashboards (Looker Studio / Power BI / Tableau) for key metrics: sales, margin, ad ROAS, refund %, inventory aging
• Create live tools to support departments (e.g., SKU performance ranking, pricing trackers, inventory planning models)
• Deep-dive analysis into product, platform, and customer data to drive strategy
• Build repeatable automations and scalable queries
• Work closely with the founder and department heads to answer real business questions

What We’re Looking For:
• Experience in both data engineering (SQL, APIs, ETL) and data analysis (insights, reports, business logic)
• Strong understanding of e-commerce operations and performance metrics
• Fluent in Python, SQL, Sheets/Excel, and dashboard tools
• Bonus: experience with BigQuery, Firebase, Shopify API, or Make/Zapier automation
• Can work during Singapore time zone (SGT) — even partially — and communicate clearly with a fast-paced team
• Resourceful, independent, and outcome-driven

Ideal Candidate:
You’re the kind of person who can spot a messy data process and rebuild it overnight. You care about making business decisions smarter, not just crunching numbers. You’re technical but also strategic. You want a seat at the table — not a ticket in a backlog.

To Apply:
Send us:
1. A short intro (highlight relevant e-commerce or similar data work)
2. Examples of past dashboards/tools you’ve built
3. Your availability (timezone, hours/week)
4. Your hourly or monthly rate (open to both models)

- Karla Mae Campollo
HR Admin | Boughtnex
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