Data Analyst/engineer

5 days ago


Toa Payoh, Singapore Grain Full time

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. - 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)** - Intro (45m): values, motivations, how you approach messy data. - Technical deep dive (60-90m): SQL/design exercise + performance tuning discussion. - Take-home (3-4h max): model a tiny domain in dbt + a dashboard; include tests & docs. - Stakeholder panel (45m): walk-through, trade-offs, storytelling. - References.



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