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Melotech is a media and entertainment company that has grown to 3 billion minutes of content consumed in 24 months. Founded by entrepreneur Soheil Mirpour and backed by Cherry Ventures, Speedinvest, and GFC, the company is building technology-driven media experiences.
As Backend Engineer - Data & Orchestration, you will own the data platform that powers the entire company. This is not a pure ETL or warehouse role—you work across data pipelines and the backend services they depend on, turning a fast-growing system into one the whole company can rely on.
Key responsibilities include:
- Data Collection: maintaining scrapers and platform integrations across flaky sources, rate limits, anti-bot measures, and APIs that change frequently
- Pipelines and Storage: owning event-driven pipelines end-to-end, from orchestration (Airflow, Dagster, or similar) to databases and warehouses, designed for failure resilience and cost efficiency
- Monitoring and Reliability: building monitoring and alerting (Datadog or similar) for services and integrations, with checks on freshness, volume, schema, and values to catch issues before downstream impact
- Backend Services: working on production services and APIs that data flows through, including JavaScript backends
- Infrastructure and Security: owning CI/CD, infrastructure-as-code, cloud systems for data, and access/secrets management
- Simplification: consolidating fast-grown systems into documented, maintainable solutions
- Cross-team Communication: explaining data capabilities and limitations to non-technical colleagues, guiding junior engineers
You will have complete ownership of these systems—not maintaining legacy code, but deciding how systems should work and making it happen. The role offers a flat hierarchy where your voice matters, ideas get implemented, and impact is immediate.
The company works remotely with regular global offsites for strategy and team building. Compensation includes competitive salary and equity ownership in the business.
REQUIREMENTS:
- 3 to 10 years of hands-on engineering, primarily in small or fast-growing companies where you owned production systems
- Advanced Python, including async code and long-running data jobs; backend experience with production services and APIs; basic JavaScript or TypeScript capability
- Proven experience keeping unreliable external sources running in production (scrapers, platform APIs, unofficial endpoints); knowledge of proxies, rate limits, anti-bot measures; LLM-based data extraction is a plus
- Design and operation of event-driven, fault-tolerant pipelines handling millions of records daily, using Airflow, Dagster, or task queues; experience with Postgres, ClickHouse, MongoDB, or Redshift; understanding of operational costs
- Data reliability and monitoring expertise: systems with freshness, volume, schema, and value checks; proficiency with Datadog or equivalent
- Comfort with Docker, Terraform, CI/CD, and cloud platforms; ideally experience with access reviews, role-based access control, and secrets management
- Demonstrated ownership of end-to-end messy data setups at smaller companies, leaving them simpler and documented; ability to explain trade-offs to non-technical stakeholders and guide junior engineers or lead small teams
- Strong engineering fundamentals developed before AI coding tools; ability to use AI tools to accelerate work while maintaining judgment
- Thrives in fast-paced, performance-oriented environments
