Backend engineer in data platforms

Melotech is revolutionizing media and entertainment. We create art through technology for humans to enjoy. In just 24 months, our work has been heard, watched and loved for over 4 billion minutes worldwide. Everything we ship runs on data, and the systems that collect, move and store that data need an owner. As Backend Engineer - Data & Orchestration, you take a data platform that grew fast and turn it into one system the whole company can rely on.

You keep it running, you keep it clean, and you make it simpler every month. This is not a pure ETL or warehouse role: you work across the pipelines and the backend services they depend on.

• Data collection: keeping our scrapers and platform integrations running across flaky sources, rate limits, anti-bot measures and APIs that change overnight
• Pipelines and storage: owning our event-driven pipelines end to end, from orchestration (Airflow, Dagster or similar) to databases and warehouses, built to survive failures and to keep costs in check
• Monitoring and reliability: building the monitoring and alerting (Datadog or similar) that keeps our services and integrations healthy, and the checks on freshness, volume, schema and values that catch a wrong number before anyone downstream notices
• Backend services: working on the production services and APIs our data flows through, including our JavaScript backends
• Infrastructure and security: owning CI/CD, infrastructure as code and cloud for our data systems, plus access and secrets: who and what can reach which system, including AI tools
• Simplification: consolidating what grew fast into one documented system, and removing what is no longer needed
• Working across the team: explaining what the data can and cannot do to non-technical colleagues, and guiding junior engineers as the team grows

• Experience:
  
  3 to 10 years of hands-on engineering, most of it in small or fast-growing companies where you owned production systems yourself rather than handing them to a platform team
• Python and backend: advanced Python, including async code and long-running data jobs, and enough backend experience to work on production services and APIs, including basic JavaScript or Type Script backends
• Scraping and data acquisition: you have kept unreliable external sources running in production (scrapers, platform APIs, unofficial endpoints) and you know how to handle proxies, rate limits and anti-bot measures
• Pipelines and storage: you have designed and run event-driven, fault-tolerant pipelines handling millions of records a day, with Airflow, Dagster or task queues, on databases such as Postgres, Click House, MongoDB or Redshift, and you know what they cost to run
• Data reliability and monitoring: your systems report their own problems through freshness, volume, schema and value checks, and you use Datadog or an equivalent to keep services and integrations reliable, because a wrong number that nobody noticed is the outcome you design against
• Infrastructure and security: you are comfortable with Docker, Terraform, CI/CD and cloud, and ideally you have run an access review and set up role-based access and secrets management
• Ownership and communication: you have owned a messy data setup end to end at a smaller company and left it simpler, documented and maintainable; you explain trade-offs to non-technical colleagues, push back when it matters, and have guided junior engineers or led a small team
• Engineering fundamentals: you learned to build solid systems before AI coding tools were everywhere, and today you use them to go faster, not to replace that judgment; you ship fast, keep the codebase clean, and take the time to understand the context before you build
• Pace: you thrive in a fast-paced and performance-oriented…