Highlevel
From Highlevel's careers page
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Our People With over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership.
HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes. To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently. Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.
Our People With over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.
Our Impact Every month, HighLevel enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact. We’re proud to be a part of that. Learn more about us on our YouTube Channel or Blog Posts
We are looking for a Staff Engineer – Backend to help design, build, and evolve the systems powering HighLevel’s CRM platform. This is a senior individual contributor role for an engineer who can solve complex backend and distributed systems problems, influence architecture across teams, and raise the engineering bar across the organization.
You will work on systems handling high-volume APIs, asynchronous processing, large datasets, search and indexing, event-driven workflows, and business-critical operations at scale. This role requires someone who can move comfortably between architecture and implementation. You should be able to investigate a production bottleneck, design a scalable solution, challenge existing architectural assumptions, and work hands-on with engineers to take that solution to production.
Design and evolve highly scalable, reliable backend systems and services
Lead architecture for complex and cross-team backend initiatives
Design APIs, data models, event-driven workflows, and service boundaries
Build systems capable of handling high-throughput requests and large-scale datasets
Design asynchronous processing using queues, events, streams, and background workers
Make architectural decisions around relational databases, NoSQL databases, search systems, caching, and messaging infrastructure
Identify performance bottlenecks across applications, databases, queues, and infrastructure
Improve API latency, throughput, resource utilization, and system scalability
Design systems for graceful degradation, retries, idempotency, failure recovery, and fault tolerance
Establish patterns for data consistency and distributed workflows across services
Lead database and data-access optimization initiatives
Design effective caching, indexing, partitioning, sharding, and data-retention strategies
Improve observability through metrics, logs, tracing, dashboards, and actionable alerts
Lead investigations into complex production incidents and drive permanent corrective actions
Define engineering standards around reliability, scalability, testing, security, and maintainability
Review architecture proposals and critical code changes across teams
Identify systemic technical debt and drive pragmatic modernization initiatives
Partner with Product and Engineering leadership to make technical trade-offs based on business requirements
Mentor senior engineers and develop stronger technical ownership across teams
Remain hands-on and contribute code to critical systems and initiatives
8+ years of software engineering experience, with significant experience building backend systems at scale
Strong proficiency in at least one backend language such as Node.js/TypeScript, Go, Java, or similar
Strong understanding of distributed systems and microservice architecture
Deep understanding of API design and service-to-service communication
Strong knowledge of relational and NoSQL database systems
Experience designing systems involving large datasets and high request volumes
Strong understanding of concurrency, asynchronous processing, queues, and event-driven architectures
Experience with caching and distributed caching strategies
Strong understanding of database indexing, query optimization, transactions, and data consistency
Experience designing reliable systems with retries, idempotency, timeouts, rate limiting, and failure recovery
Strong understanding of scalability, availability, consistency, and performance trade-offs
Experience diagnosing complex production issues using metrics, logs, and distributed tracing
Strong system design and architectural decision-making skills
Demonstrated ability to lead technically complex initiatives involving multiple engineers or teamsPreferred Qualifications
Strong experience with Node.js / TypeScript and Go
Experience operating distributed systems at significant scale
Experience with PostgreSQL, MySQL, MongoDB, Firestore, or similar databases
Experience with Kafka, Pub/Sub, RabbitMQ, SQS, or similar messaging systems
Experience designing search, indexing, aggregation, and reporting systems
Experience with high-volume event processing and data pipelines
Experience with AWS, GCP, or similar cloud platforms
Experience with observability platforms such as Grafana, Prometheus, OpenTelemetry, or similar tooling
Experience with CRM, workflow automation, task management, messaging, or similar products
Experience performing large-scale data migrations without significant customer Impact
Tech Stack
Backend: Node.js, TypeScript, Go
Databases & Search: MySQL, MongoDB, Firestore, Elasticsearch/OpenSearch
Caching: Redis
Messaging & Events: Kafka, Pub/Sub
Architecture: Microservices, REST APIs, Event-Driven Systems
Infrastructure: Kubernetes, GCP
Observability: Metrics, Logs, Distributed Tracing and Alerting
What Success Looks Like
Designs systems that remain reliable as traffic and data volume grow significantly
Solves architectural problems that affect multiple services and engineering teams
Identifies scalability and reliability risks before they become production incidents
Improves latency, throughput, availability, and infrastructure efficiency
Makes strong technical decisions around data storage, caching, messaging, and service architecture
Simplifies complex systems rather than introducing unnecessary abstractions
Creates reusable architectural patterns that improve engineering velocity across teams
Leads ambiguous technical initiatives from problem definition through production
Raises the quality of system design and technical decision-making across the organization
Improves operational excellence through better observability, incident prevention, and failure recovery
Mentors engineers to independently solve increasingly complex systems problems
Remains hands-on and capable of going deep into production systems when required
We are looking for engineers who think about systems beyond the happy path.
What happens when this system handles 10x the current traffic?
What happens when a dependency becomes slow or unavailable?
How does this behave when millions of events arrive within a short period?
What are our consistency guarantees, and where can we tolerate eventual consistency?
How do we prevent duplicate processing and make operations idempotent?
Is the database the bottleneck, or are we solving the problem at the wrong layer?
Should this data live in a transactional database, search engine, cache, or event stream?
How do we migrate this architecture without disrupting existing customers?
How will we observe this system in production and know when it is degrading?
Can we solve this once at the platform level instead of every team solving it independently
The Staff Engineer role is not defined by writing the most code. It is defined by identifying the highest-leverage technical problems, making strong architectural decisions, solving the hardest parts, and enabling engineering teams to build reliable systems faster.
Build backend systems operating at significant global scale
Solve challenging distributed systems and data architecture problems
Influence architecture across multiple products and engineering teams
Work with high-volume APIs, event streams, databases, search systems, and distributed infrastructure
Drive improvements in scalability, reliability, performance, and engineering efficiency
Remain deeply technical without moving into people management
Work in a fast-moving engineering environment with significant autonomy and Ownership
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