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Grain career site
Engineering · Singapore · Hybrid

Data Analyst/Engineer

You tame messy data, model truth, and ship dashboards people actually use. Fast clock speed, sleeves rolled. Build our Data Hub so every team pulls from one clean, performant source—freeing product engineers to ship product.

Mission

Own the pipelines, models, and metrics layer that power decisions at Grain. Turn chaos into clarity: reliable datasets, crisp dashboards, and self-serve tools that scale.

Outcomes (first 90 days)

  • Data Hub foundation: documented sources, core datasets modelled and tested
  • Leadership has reliable data for top decision areas (such as demand forecasting, breakdown of product and channel performance, customer retention, etc.) — no more "let me check and get back to you"
  • Team productivity: 30% reduction in ad-hoc requests as teams self-serve common questions

Responsibilities

  • Build data pipelines: pull data from our systems (sales, inventory, finance, marketing), clean it, and make it queryable
  • Create the metrics layer: define key business metrics once so everyone uses the same definitions (no more "which revenue number is right?")
  • Ship dashboards people use: fast, clear visualisations that answer real questions; teach teams to find their own answers
  • Keep it fast and cheap: optimize queries, manage warehouse costs, monitor performance
  • Ensure quality: write tests, set up alerts when data breaks, establish freshness expectations
  • Protect customer data: handle PII safely, control who sees what, maintain audit trails
  • Push data where it's needed: send clean data back to sales, marketing, and support tools
  • Raise the bar: write docs people actually read, run demos, make everyone more data-literate

Competencies

  • Strong SQL: efficient, readable queries that answer tough questions. You understand execution plans and can optimize slow queries
  • Data modeling: fact/dimension tables, slowly changing dimensions, event schemas - and when to break the rules
  • Modern stack: dbt + orchestration (Airflow/Dagster/Prefect), cloud warehouse (BigQuery/Snowflake/Redshift/Postgres)
  • BI tools: Built dashboards people actually use (Looker, Metabase, Superset, or similar)
  • Quality: Testing, lineage, monitoring - you catch issues before stakeholders do
  • Stakeholder work: Turn "show me engagement" into concrete metrics and actionable insights
  • Bonus points: Python scripting, CI/CD, infrastructure-as-code (Terraform), event tracking, privacy-by-design thinking

How we work

  • Problem → Prototype → Prove value → Productionise.
  • One source of truth > many spreadsheets.
  • Stewardship over flash: reliable, observable, cost-aware.
  • We value integrity, excellence, service—use data to uplift people.

What’s in it for you

  • Autonomy and ownership of the data stack.
  • Ship work used daily by every function.
  • Mentorship, growth into Staff/Analytics Eng or Data Platform Eng path.
  • Competitive compensation and birthday leave.

What to include in your application

  • CV or LinkedIn + GitHub (if any).
  • 2–3 dashboards or repos you’ve built (screenshots/links) with a short note on impact.
  • A one-pager: your approach to building a “Data Hub” in 90 days.

Interview process (typical)

  1. Intro (45m): values, motivations, how you approach messy data.
  2. Technical deep dive (60–90m): SQL/design exercise + performance tuning discussion.
  3. Take-home (3–4h max): model a tiny domain in dbt + a dashboard; include tests & docs.
  4. Stakeholder panel (45m): walk-through, trade-offs, storytelling.
  5. References.
Department
Engineering
Locations
Singapore
Remote status
Hybrid
Employment type
Full-time

WHAT’S THE INTERVIEW PROCESS LIKE?

The process can differ between roles, but we usually start with a quick screening call followed by in-person interviews to be sure we’re on the same page and a good fit for each other.

1. Quick 45 minute screening interview to make sure we are on the same page. Here, we try to briefly understand cultural fit, where you’re at, what you’re optimising for, strengths and weaknesses, relevant experience, etc.
2. 1–1.5 hour deeper interview with the functional lead. There may be some overlapping questions here — we don’t compare notes between interviews to keep the process independent.
3. [Only certain roles] 3–4 hour practical interview.
4. [Only certain roles] Interview with CEO/COO.
5. Reference check.

If you have read this far, some useful tips:

• Always follow up after each interview - tell us things you learned, what you enjoyed, why you would be a good fit, ask more questions, supplement your answers — this will go a long way!
• This is a basic detail but people fail at it and we are the kind of company that likes helping you out — reply all in your emails. Make sure everyone that needs to be in the loop (especially your champions in this case!) are to/cc-ed in each thread.
• “Any questions for me?” → The right answer is always yes. And belt out the 3 questions you prepared for this interview. Some good questions: “How did I do in this interview? Please give me brutal feedback.”, “Pre-mortem: If I joined Grain and I was no longer with you all in 6-12 months. Why?”, “What do you think are the most important traits for this job?”
• Pro tip for sales interviews: Sales is more about asking questions, instead of talking. Learn this in 1:38 minutes here.
Engineering · Singapore · Hybrid

Data Analyst/Engineer

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