Senior Backend Engineer (Data Core Team) at Modash

Why we're hiring

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.

What you'll actually own

1. Keep creator data flowing at massive scale.

You’ll build and evolve the systems that collect and maintain 400M+ creator profiles across Instagram, TikTok, and YouTube—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.

What the day-to-day looks like

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 short standup, close collaboration when it helps, and plenty of space to design, build, harden, and operate systems.

Requirements

What you've done before

• Built large-scale backend or data systems. You have solid experience with high-volume services where reliability, throughput, latency, and cost all matter.
• Shipped systems from concept to production. You’ve owned scoping, architecture, implementation, release, operation, and iteration—not just one layer of the solution.
• Designed distributed systems. You can reason clearly about partial failure, retries, idempotency, backpressure, scaling, and operational tradeoffs.
• Built resilient integrations. You’ve worked with third-party APIs or other external dependencies that are rate-limited, unstable, or liable to change.
• Worked with proxy management or request routing. You understand the practical challenges of routing high-volume traffic and navigating anti-bot systems responsibly at scale.
• Taken operational ownership. You care about observability, alerting, runbooks, and what happens after deployment—not just whether the code merged.
• Worked autonomously on ambiguous problems. You know how to gather requirements, ask useful questions, and make progress without waiting for a perfectly specified ticket.
• Communicated clearly across teams. You can explain tradeoffs, give direct feedback, and collaborate without creating unnecessary process.

Bonus points if you’ve worked with social-media APIs, data-quality frameworks, large-scale collection systems, or freshness and coverage monitoring. Curiosity about the creator economy helps too, but we’ll get you up to speed.

Our stack

• AWS, with Pulumi for infrastructure as code
• TypeScript and Node.js as the primary languages
• Aurora Limitless/Postgres for subscription and collection state
• S3 for raw data-lake storage
• CloudWatch for observability and alerting
• Proxy networks and custom routing layers
• ECS, SQS, Athena, and other AWS services
• Slack, GitHub, Linear, Notion, and Cursor