Note: The job is a remote job and is open to candidates in USA. Melotech is hiring a Backend Engineer - Data & Orchestration to own and simplify the data platform that supports the company. The role spans data collection, event-driven pipelines and storage, production backend services, monitoring, infrastructure, security, and collaboration across the team.
Responsibilities
- 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
Skills
- 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
- 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 TypeScript backends
- 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
- Using LLMs to extract data from messy sources is a plus
- 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, ClickHouse, MongoDB or Redshift, and you know what they cost to run
- 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
- You are comfortable with Docker, Terraform, CI/CD and cloud
- Ideally you have run an access review and set up role-based access and secrets management
- 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
- 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
- You thrive in a fast-paced and performance-oriented environment
- Ideally you have run an access review and set up role-based access and secrets management
Benefits
- Equity ownership in the business
- Remote work
- Regular global offsites
Company Overview