Too Good To Go · jobs.ashbyhq.com · checked today
Senior Data Platform Engineer
At Too Good To Go, we have an ambitious goal: to inspire and empower everyone to fight food waste together. 40% of all food produced in the world is wasted, contributing to 10% of global greenhouse gas emissions. We’re more than an app, we are a social impact company working across 20 countries to drive change through our marketplace app, educational tools, legislative work, and logistics initiatives.
Skills, with evidence
- Airflow / orchestration
Extensive experience designing, building, and operating data processing solutions (Airflow, AppFlow, Metaflow, dbt, dlt), and working with distributed systems in production (Spark on EMR, Glue).
must have - Python
Data Engineering: Deep proficiency in Python and SQL, advanced understanding of modern data warehouse internals (AWS Redshift), efficient ways of working on S3, and strong data engineering fundamentals including scalability, reliability, fault tolerance, and trade-off analysis.
must have - SQL
Data Engineering: Deep proficiency in Python and SQL, advanced understanding of modern data warehouse internals (AWS Redshift), efficient ways of working on S3, and strong data engineering fundamentals including scalability, reliability, fault tolerance, and trade-off analysis.
must have - Spark
Extensive experience designing, building, and operating data processing solutions (Airflow, AppFlow, Metaflow, dbt, dlt), and working with distributed systems in production (Spark on EMR, Glue).
must have - dbt
Extensive experience designing, building, and operating data processing solutions (Airflow, AppFlow, Metaflow, dbt, dlt), and working with distributed systems in production (Spark on EMR, Glue).
must have - AWS
Optimize system performance, efficiency, and infrastructure cost governance across our AWS data ecosystem.
must have · not practised here - Terraform
Experience applying DataOps, GitOps, Terraform, CI/CD tools (GitHub Actions, Docker), and observability tools.
must have · not practised here - Streaming
Data Platform enables multiple teams including Backend, ML, Analytics, and Marketing by helping them manage data infrastructure powering batch pipelines, live inference, and streaming initiatives across the company.
- Data quality
- Automate observability metrics, monitoring, and data integrity tests to ensure reliable, trusted data sources and strong data governance across central warehouse and external team product workflows.
- Failure handling
- Automate observability metrics, monitoring, and data integrity tests to ensure reliable, trusted data sources and strong data governance across central warehouse and external team product workflows.
- Warehousing
- Automate observability metrics, monitoring, and data integrity tests to ensure reliable, trusted data sources and strong data governance across central warehouse and external team product workflows.
- Dashboards & BI
You will be responsible for designing, improving, and implementing reliable, robust data systems and the ingestion flows that feed both our backend app and our BI tools, supporting growth and better decisions across the company.
not practised here - Governance & security
- Optimize system performance, efficiency, and infrastructure cost governance across our AWS data ecosystem.
not practised here - Kubernetes
Experience applying DataOps, GitOps, Terraform, CI/CD tools (GitHub Actions, Docker), and observability tools.
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
SQL · Data quality
- Median delivery time per cityIntermediate
- Bucket deliveries into quartilesIntermediate
- Median order value without a median functionIntermediate
- New and repeat orders by monthIntermediate
- Every order against its customer's averageIntermediate
- Python: the data-wrangling round≈ 3 h
Python
- Diff two snapshots of a tableIntermediate
- Explode an array column into rowsIntermediate
- Flatten nested event payloadsIntermediate
- Keep only the latest row for each eventIntermediate
- Pivot a long metrics table to wideIntermediate
- Data modelling: the round most people fail≈ 3 h
dbt · Warehousing
- Addresses that stay true to the pastIntermediate
- Subscription warehouse grainIntermediate
- Campaign efficiencyIntermediate
- Chats, members and read receiptsIntermediate
- Churn that survives an argumentIntermediate
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration · Streaming · Failure handling
- The source will not let youIntermediate
- Parcel tracking pipelineIntermediate
- Is this change safe?Intermediate
- Marketplace transactions at scaleIntermediate
- Five minutes behind the sourceIntermediate
- Spark: read the plan Spark actually ran≈ 2 h
Spark
- broadcast() with auto-broadcast off, and the case where Spark ignores itIntermediate
- autoBroadcastJoinThreshold compares an estimate: flip a join with select() and one settingIntermediate
- Does Spark really run your EXISTS subquery once per row?Intermediate
- left_semi and left_anti: "customers who did / never did" without a full joinIntermediate
- A self-join on a real key that still multiplies rowsIntermediate
- Say it out loud≈ 1 h
25 drills · Intermediate + Advanced≈ 14 hours
Not covered by the plan: AWS, Terraform, Dashboards & BI, Governance & security, Kubernetes.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 5 dremoved the moment Too Good To Go closes it