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Remote (Pakistan preferred) · Full-time
🧭 About the role
iClosed .io is a SaaS platform for sales and marketing teams, scaling to millions of users. This role sits in our Performance Team and owns the performance and scalability of our Node.js/TypeScript backend services and data layer.
We believe the biggest gains come from design, not just tuning: matching each store to its access pattern, moving work off the critical path, and drawing service boundaries that scale cleanly. You'll solve the problem in front of you, then shape the architecture so whole classes of problems never come up.
This is a senior individual contributor role with no direct reports, high autonomy and significant technical influence.
🔀 How the work splits
You'll own performance across three connected areas, with genuine depth expected in both system design and data-layer internals:
Design: how a request flows, what runs synchronously, where state lives, and what happens when a dependency is slow.
Diagnosis: reading traces, execution plans and metrics to find the real cause rather than the obvious one.
Prevention: instrumentation, review standards and regression gates, so issues are caught long before they reach customers.
🎯 What you'll do
Lead the design of high-traffic flows end to end, keeping them fast under load and resilient when dependencies fail.
Diagnose latency across the full request path using traces, execution plans and metrics.
Match each workload to the right data store, and document the reasoning.
Scale reads and writes through caching, replicas, partitioning, sharding and queue-buffered writes.
Build reliable background processing on SQS and EventBridge with idempotency, retries, dead letters and backpressure.
Eliminate N+1 queries and chatty data access, including inefficient queries generated by ORMs such as Prisma and Mongoose.
Own data-layer capacity planning and cost per request alongside latency.
Set engineering standards: design and query review for hot paths, migration standards, CI regression gates, and load testing at production-realistic volumes.
Review architecture proposals from feature teams, and write the design docs others build from.
🧑💻 Who we're looking for
Required
5+ years building and operating production backend systems, with 3+ where performance and scale were a named part of your job.
Performance ownership of a multi-tenant SaaS product under real load.
Systems you designed yourself, including the alternatives you rejected and the tradeoffs you accepted.
A concrete track record: a problem you diagnosed, its root cause, your fix, and the before-and-after numbers.
Strong system design judgement across caching, sharding, CQRS, queues, real-time delivery (WebSockets/SSE), autoscaling, high availability and resilience patterns. Knowing when not to use one matters more to us than listing its benefits.
Real depth in at least two of PostgreSQL/MySQL, MongoDB, DynamoDB and Redis, with working competence in the rest.
Production ownership of Node.js/TypeScript services, and a solid understanding of how ORMs and ODMs such as Prisma and Mongoose generate queries.
Hands-on Datadog APM and tracing experience (or equivalent), plus production experience on AWS (ECS/EKS), including the cost behaviour of managed data services.
Clear writing: design docs a team can build from and an executive can follow.
🗄️ Data layer depth Nobody is deep in all four engines, and we'd rather you tell us where you are. Across your areas of depth, we expect you to reason about:
Relational (PostgreSQL, MySQL): execution plans as routine, composite and partial indexes and their write cost, locking, isolation levels, connection pooling and replica lag.
Document (MongoDB): embedding vs referencing, shard key selection, aggregation performance and working set sizing.
Key-value: access-pattern-first modelling, partition keys, hot partition avoidance, secondary indexes and capacity cost.
In-memory cache (Redis/Memcached): caching patterns, key design, eviction and TTL, and system behaviour when the cache is cold or unavailable.
Across engines: consistency boundaries, dual-write problems, and recognising when a query problem is really an application design problem.
🌟 What success looks like Diagnosis: latency is attributed to real causes with evidence, and per-tenant investigations end with clear answers.
Design: critical paths have documented designs others can reason about, store selection is deliberate and written down, and queues behave predictably under load.
Measurable improvement: meaningful latency reduction on the highest-cost paths, with before-and-after numbers, and cost per request trending down.
Prevention: design and query review is standard practice for hot paths, regressions are caught before merge, and migration standards are followed across teams.
Partnership: feature teams bring you proposals early because you make them better, not slower.
➕ Nice to have
APM, error tracking and database performance tooling using tools like Datadog and Sentry · event streaming or messaging at scale · real-time delivery and fan-out at scale (Socket.IO) · third-party integrations where latency is outside your control · noisy-neighbour and tenant isolation · load testing tools and building that capability · SLOs and error budgets · infrastructure as code
🚫 What this role is not Not a DBA role. Backups, patching and routine operations sit with DevOps. You set the performance requirements.
Not feature work. Your output is latency removed and regressions prevented.
Not a management role today. Leading the function as it grows is an open conversation.
💎 Why join Shape the performance function and its priorities from day one.
Have direct influence on architecture across every engineering team.
Work on a polyglot stack at real scale.
📋 Practicalities Location: Remote (Pakistan preferred) Type: Full-time, permanent Level: Senior individual contributor, no direct reports Compensation: Market competitive On-call: Part of the escalation rota Salary: Market Competitive Originally posted on Himalayas
