Senior Backend Engineer - Data Core; remote, Europe

Remote — Data Core Team — Full-time

Hey, I'm Andrei. I’m hiring a Senior Backend Engineer to join the Data Core team at Modash.

Modash helps brands find, understand, and work with creators across Instagram, Tik Tok, and You Tube. More than 2,700 companies—including Stanley 1913, Sennheiser, and NordVPN—use us to manage and scale their creator partnerships.

Every one of those products depends on fresh, reliable creator data. Data Core owns the systems that collect and maintain profiles for 400M+ creators across social platforms, process billions of data points, and keep the rest of Modash supplied with the raw material it needs.

That’s where you come in.

Data Core isn't an internal support team. It owns the collection infrastructure that the entire Modash product—and every downstream data team—depends on.

Collecting social data at this scale is a hard distributed-systems problem. APIs change without warning. Providers become unreliable. Platforms rate-limit requests. Data grows stale. A tiny inefficiency becomes expensive when repeated hundreds of millions of times.

We need a senior engineer who can design resilient services, make thoughtful tradeoffs between coverage, freshness, reliability, and cost, and take production-critical systems from a rough idea to dependable operation.

You’ll join the specialised Data Core team and work closely with Data Search, Data Insights, product teams, and company leadership. You’ll have real ownership, but you won’t work in isolation.

If you want a feel for how we think about building software, check our Engineering Blog.

1. Keep creator data flowing at massive scale.

You’ll build and evolve the systems that collect and maintain 400M+ creator profiles across Instagram, Tik Tok, and You Tube—keeping billions of data points fresh enough for search, analytics, APIs, and customer-facing products.

2. Make collection resilient when the outside world isn't.

You’ll design services that handle third-party API instability, rate limits, provider outages, platform changes, and partial failures without turning every disruption into a customer incident.

3. Improve the economics of collection.

At this scale, every request, proxy call, retry, storage decision, and compute cycle matters. You’ll improve coverage and freshness while keeping the system financially sustainable.

4. Own production, not just the code.

You’ll shape the problem, design the architecture, write and review the code, ship it, observe it, and improve it. You’ll build the monitoring and operational safeguards that catch gaps and regressions before customers do.

Here’s what a typical week might include:

• Monday. A social platform has changed its behaviour overnight. Collection success has dropped, but only for part of the traffic. You trace the failure pattern, protect downstream freshness, and design a resilient fix rather than a brittle patch.
• Tuesday. Deep-focus time. You redesign part of the subscription system that decides which creators to collect, when, and how often—balancing customer value against request and compute cost.
• Wednesday. You pair with a Data Search engineer on an indexing dependency. Together, you agree on a cleaner contract that improves freshness without coupling the two teams’ systems.
• Thursday. You review a new request-routing approach across proxy providers. You model throughput, failure modes, and unit economics before shipping a small production experiment.
• Friday. An observability review reveals a slow coverage regression that existing alerts missed. You improve the data-quality checks so the team catches the next one before it reaches customers.

We keep meetings purposeful and protect time for deep work. You’ll have a…