Backend Engineer - Data & Orchestration

Melotech

2d•Remote

About The Position

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. We will walk you through exactly what we are building as you go through the process.

Requirements

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
Enough backend experience to work on production services and APIs, including basic JavaScript or TypeScript backends
Experience keeping unreliable external sources running in production (scrapers, platform APIs, unofficial endpoints) and knowledge of how to handle proxies, rate limits and anti-bot measures
Experience designing and running 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 understanding of their running costs
Systems that report their own problems through freshness, volume, schema and value checks
Experience using Datadog or an equivalent to keep services and integrations reliable
Comfort with Docker, Terraform, CI/CD and cloud
Experience owning a messy data setup end to end at a smaller company and leaving it simpler, documented and maintainable
Ability to explain trade-offs to non-technical colleagues, push back when it matters
Experience guiding junior engineers or leading a small team
Understanding of engineering fundamentals and ability to build solid systems before AI coding tools were everywhere
Ability to use AI coding tools to go faster, not to replace judgment
Ability to ship fast, keep the codebase clean, and take the time to understand the context before building

Nice To Haves

Using LLMs to extract data from messy sources is a plus
Ideally have run an access review and set up role-based access and secrets management

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