Staff Software Engineer - Backend

Epic Placements LLC; United States; Remote; USD 180,000 - 320,000; Full time

Benefits offered by this job

Remote work across US/Canada

Equity

Job summary

Epic Placements LLC is seeking a Senior/Staff-level Backend Engineer for a remote role across the US and Canada, building a customer-facing data product.

You will tackle massive datasets, distributed systems, and real-time architecture while owning design and implementation end-to-end. The role emphasizes ownership, collaboration with product, and delivering scalable backend services.

Compensation includes $180k–$320k base salary plus equity; visa transfers may be supported.

Qualifications

• Significant backend engineering experience building production systems.
• Experience owning projects from design to production.
• Strong problem-solving in data processing and distributed systems.
• Ability to work with product to ship customer-facing features.

Responsibilities

• Design backend systems and architecture.
• Own projects from design through production rollout.
• Balance scalability, reliability, and maintainability.
• Collaborate with product and engineering teams.
• Mentor other engineers as the team grows.

Skills

Go

Python

TypeScript/Node.js

Java

Kotlin

Rust

Distributed Systems

SQL

PostgreSQL

Data Processing

Education

Bachelor's degree in Computer Science or related field

Tools

Kafka

SQS/SNS

Pub/Sub

Kinesis

RabbitMQ

Redis

Flink

Spark

AWS

Docker

Kubernetes

Job description

Remote — US or Canada

$180K–$320K Base + Equity

Full-Time

We're partnering with a fast-growing technology company building sophisticated software on top of modern cloud data infrastructure.

They're looking for a Senior/Staff-level Backend Engineer to help build a customer-facing data product used to turn large, complex datasets into fast, intuitive workflows for nontechnical users.

This is a backend role for someone who likes hard systems problems but still cares deeply about the product those systems enable.

Why This Is Technically Interesting

The challenge is deceptively simple:

Make massive, complex datasets feel like a responsive product.

Underneath that experience are difficult engineering problems involving data processing, distributed systems, performance, reliability, and increasingly real-time architectures.

You'll work on problems like:

• Optimizing queries, caching strategies, and precomputed state
• Designing systems that process and synchronize large amounts of data reliably
• Moving traditionally batch-oriented workflows closer to real timeBuilding event-driven and change-data-capture workflows
• Developing analytics and experimentation capabilities
• Designing backend architecture that works across different customer data environments
• Exploring practical ways generative AI can improve user workflows

The systems underneath the product are complex.

The experience for the person using it shouldn't be.

That's a big part of the engineering challenge.

What You'll Own

This is not a ticket-execution role.

You'll have meaningful responsibility for technical decisions and will help:

• Design backend systems and architecture
• Own projects from initial design through production
• Make tradeoffs around scalability, reliability, performance, and maintainability
• Solve difficult data-processing and performance problems
• Work through product requirements before implementation
• Collaborate closely with product and engineering
• Use real customer feedback to influence technical decisions
• Mentor other engineers as the team continues to grow

We're looking for engineers who want ownership of both how a system works and whether it solves the right problem.

Who We're Looking For

The strongest candidates have already been trusted with difficult backend problems and significant technical ownership.

You may be a good fit if you've:

• Designed and built sophisticated backend systems
• Solved meaningful scale, performance, or reliability challenges
• Worked with large or complex datasets
• Made significant architecture and systems-design decisions
• Operated successfully in a high-growth technology environment
• Taken on increasingly difficult problems throughout your career
• Worked closely enough with a product to understand the impact of your engineering decisions on actual users
• Mentored engineers or served as a technical leader

We're much more interested in what you've built and the decisions you owned than whether your resume contains our exact technology stack.

Technical Environment

You may have experience with some combination of:

Backend: Go, Python, TypeScript/Node.js, Java, Kotlin, Rust, or similar

Data: SQL, PostgreSQL, cloud data warehouses, large-scale data platforms, query optimization, data modeling, caching, and precomputation

Distributed Systems: Event-driven architecture, asynchronous processing, concurrency, high-throughput services, workflow orchestration, CDC, batch processing, and streaming systems

Messaging / Streaming: Kafka, SQS/SNS, Pub/Sub, Kinesis, RabbitMQ, Redis, Flink, Spark, or similar technologies

Cloud / Platform: AWS, GCP, Azure, Docker, Kubernetes, observability, and production reliability tooling

Product Backend: APIs, multi-tenant SaaS architecture, integrations, analytics, experimentation, and customer-facing backend services

You do not need experience with every technology listed above.

Strong systems fundamentals and evidence of solving difficult engineering problems matter considerably more than checking every technology box.

What Will Stand Out

We're particularly interested in engineers who can point to things like:

• Improving database or query performance
• Building high-throughput APIs and backend services
• Designing storage or data-processing architecture
• Building event or stream-processing systems
• Solving difficult concurrency, consistency, latency, or reliability problems
• Owning critical customer-facing backend infrastructure
• Taking a system from initial architecture through production and scale

When you describe your work, tell us what was difficult, what you personally owned, and what changed because of it.

That's much more interesting than a list of technologies.

What This Role Isn't

This probably isn't the right opportunity if you're primarily looking for:

• Frontend engineering
• DevOps or SRE
• Internal tooling
• Basic application/CRUD development

This also isn't an AI-only position.

There will be opportunities to work on emerging AI-enabled product experiences, but sophisticated backend, data, and systems engineering remains at the heart of the role.

Product Mindset Matters

Technical depth alone isn't enough.

You'll be working on backend systems that directly determine what customers can do with the product.

That means thinking about:

• Why something is being built
• How customers actually use it
• Product requirements and tradeoffs
• How architecture affects the user experience
• Reliability from the customer's perspective
• Whether added technical complexity actually creates value

You’ll work closely with product partners and may occasionally engage directly with users to better understand the problems you’re solving.

Experience

We're primarily targeting experienced Senior and Staff-level engineers.

Strong candidates will generally bring:

• Significant professional backend engineering experience
• Evidence of increasing technical ownership
• Experience building production systems at meaningful scale
• Experience in a fast-moving, high-growth engineering environment

A technical degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field is preferred.

We're more interested in demonstrated engineering ability and trajectory than enforcing an arbitrary years-of-experience cutoff.

Remote

This is a remote opportunity available across the US and Canada.

The broader engineering organization is distributed, and the position is designed to operate effectively in that environment.

Candidates who prefer occasional in-person collaboration may also have access to a US office.

Compensation

The overall package also includes meaningful equity.

Visa transfers may be supported for qualified candidates.

Interview Process

Initial Conversation — 30 minutes

Background, interests, and what you're looking for next.

Your experience, ownership, operating style, and approach to engineering.

System Design — 45 minutes

Work through the design of a data-processing feature from end to end.

Work through a broader system-design problem involving technical, product, and user requirements.

The goal isn't to test whether you've memorized a particular technology stack.

It's to understand how you reason about real systems.

The Short Version

If you've built difficult backend systems, enjoy solving data and distributed-systems problems, care about the product your systems enable, and want significant ownership without giving up remote flexibility, we'd like to talk.