{"id":3477055,"url":"https://alion.io/job/too-good-to-go-senior-data-platform-engineer","title":"Senior Data Platform Engineer","company":{"id":55352,"name":"Too Good To Go","domain":"toogoodtogo.com","url":"https://alion.io/company/too-good-to-go","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":84,"open_postings":8,"ghost_share":0,"stale_share":0.625,"repost_share":0,"time_to_fill_p50_days":40,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Copenhagen, Denmark"],"countries":["DK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":73000,"max_usd":193000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"Flink","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitOps","optional":false},{"name":"Go","optional":false},{"name":"Metaflow","optional":false},{"name":"OpenSearch","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Helm","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-29T15:42:47Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-01T07:25:14Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About Too Good To Go\nAt 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.\nOur community of 118 million registered users and 246,000 active partners, have together already prevented 443+ million meals from going to waste - avoiding over 1.198.000 tonnes of CO2e!\nThe Role\nToo Good To Go is looking for a Senior Data Platform Engineer to help build, scale, and architect our data infrastructure, enabling our global mission to reduce food waste.\nYou would be part of the Platform team based in Copenhagen HQ. 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. In this role, you will operate as a technical anchor at the intersection of platform infrastructure and data engineering, scaling the ecosystem to meet the needs of the teams building on it. 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.\nThis is a senior role for someone who enjoys solving complex infrastructure problems, driving architectural vision, and mentoring others. You will work closely with analytics engineers, ML engineers, and product managers to build practical solutions for problems that don't have a standard answer, and to scale the platform that serves many teams.\nTech Stack\nOur data stack includes AWS (Redshift, Lambda, Kinesis, Firehose, S3, Glue, EMR, AppFlow), Airflow, Flink, dlt, dbt, and Metaflow, built and managed with Python and Terraform following GitOps principles. We also provide access to modern AI tools to boost engineering productivity.\nKey Responsibilities\nDesign, architect, and maintain scalable data processing solutions with a focus on reusable infra systems, building services that enable external teams to power personalization, product discovery, lead generation, dynamic pricing, and BI dashboards.\n\nOwn the shared infrastructure that other teams build on, including ML workflows (Metaflow), the data processing platform (Glue, AppFlow, Airflow, Lambda), and data storage (Redshift, OpenSearch, RDS, S3).\n\nBuild self-service tooling and infrastructure standards so teams can deploy and operate their own pipelines independently.\n\nOptimize system performance, efficiency, and infrastructure cost governance across our AWS data ecosystem.\n\nPartner closely with Product Managers and Tech Leads within ML, Finance, Marketing, and Analytics domains to translate operational use cases into clear, robust technical requirements to improve our product.\n\nAutomate 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.\n\nDrive data engineering best practices, conduct technical design reviews, and mentor engineers across the broader data team.\n\nWhat We're Looking For\nData 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.\n\nProduction pipelines: 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).\n\nDataOps: Strong software engineering and systems engineering skills, with a proven record of managing platform-level systems end-to-end. Experience applying DataOps, GitOps, Terraform, CI/CD tools (GitHub Actions, Docker), and observability tools. Familiarity with MLOps principles, cloud-native infrastructure, and container orchestration.\n\nTechnical Leadership: Proven ability to take some technical ownership of complex projects in autonomy, drive architectural decisions, and mentor team members in technical best practices.\n\nNice to have: Streaming experience (Kinesis, Firehose, Flink), Kubernetes and Helm experience.\n\nWhat We Offer\nOpportunity to work at a scaling social impact company and directly reduce food waste globally.\n\nWork-life balance focus (no mandatory overtime or standard on-call duty).\n\nHybrid work setup in our Copenhagen HQ (4 days in office).\n\nTo be part of an international company, with over 1,300+ colleagues across 19 countries that are on the same important mission.\n\nAn informal environment, working alongside 180+ passionate people within Tech & Product\n\nWe celebrate our differences and our successes and have a strong values-driven team culture.\n\nOur values\nWe win together: Food waste is a big beast to fight. We believe in a #oneteam.\nWe raise the bar: We always push for more. We work smart, smash barriers and elevate one another.\nWe keep it simple: Our ambitions are bold but our solutions are simple.\nWe build a legacy: We’re proud of the change we’re driving.\nWe care: We always look out for each other. Caring is also about the way we do business. We do the right thing.\nHow To Apply\nWe take recruitment very seriously, so please carefully read everything we have written above.\nPlease also check our website and international media in order to get a good overview of Too Good To Go.\nPlease submit your CV/resume in English.\nPlease note that we only accept applications coming through our platform. 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