Architect and build a scalable frontend platform using React and TypeScript for a Kubernetes-native data product.
Posted by employer 10 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In the role of Principal Frontend Engineer at Acceldata, the individual will architect and develop a scalable frontend platform using React and TypeScript, focusing on data observability and Kubernetes-native products. Key skills include expertise in frontend architecture, performance optimization, and building data-intensive user interfaces, alongside a solid foundation in JavaScript and API integration. This position is ideal for seasoned engineers with 12-15 years of experience who are adept at mentoring and establishing best practices in a fast-paced, early-stage environment.
Quick facts
- How much experience is required?
- 12–15 years of relevant experience for this Principal Frontend Engineer role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Apache Spark, CSS, HTML, JavaScript, Kubernetes, Material UI.
- What seniority level is this role?
- Acceldata targets principal-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Principal Frontend Engineer role at Acceldata.
From the original posting
Acceldata is reimagining the way companies observe their Data!
Acceldata is the pioneer and leader in data observability, revolutionizing how enterprises manage and observe data by offering comprehensive insights into various key aspects of data, data pipelines and data infrastructure across various environments. Our platform empowers data teams to manage products effectively by ensuring data quality, preventing failures, and controlling costs.
A day in the life of a Principal Frontend Engineer
Architect and build a scalable frontend platform using React and TypeScript for a Kubernetes-native data product.
Design intuitive UIs for:
-Kubernetes cluster visibility (nodes, pods, workloads).
-Spark job lifecycle management (submission, monitoring, debugging).
-Data pipeline observability and performance metrics.
Define frontend patterns for:
-Real-time data streaming, polling, and state synchronization
-Handling large datasets (tables, logs, metrics, traces)
Collaborate with backend teams to integrate with:
-Kubernetes APIs (resources, events, logs)
-Spark execution and monitoring systems
Build high-performance, data-intensive dashboards with efficient rendering and virtualization.
Establish a lean, extensible frontend architecture suitable for an early-stage product (avoid over-engineering).
Define API contracts, data-fetching strategies, and error-handling standards for distributed systems.
Optimize frontend performance (lazy loading, code splitting, caching, minimizing re-renders).
Implement frontend observability (error tracking, performance monitoring) for production systems
Mentor engineers and establish frontend best practices, coding standards, and design guidelines
You are a great fit for the role, if you have
12-15 years of experience in building modern, scalable frontends using frameworks such as Angular, React or Typescript.
Strong experience in frontend architecture & system design (modular design, state management, API layers).
Proven experience building data-intensive UIs (dashboards, large tables, logs, analytics tools).
Expertise in JavaScript / TypeScript, HTML, CSS, Material UI, Vue.JS.
Develop and integrate backend services using Node.JS
Write unit and integration tests using Jasmine and Karma to ensure code quality.
Strong performance optimization skills (rendering, virtualization, lazy loading, re-render control).
Solid experience with API integration & async data handling (pagination, retries, error states).
Experience handling real-time / near real-time data in frontend applications.
Strong code quality practices (clean code, code reviews, testing).
Work efficiently with development tools such as VS Code and Cursor.
Good to have
Working knowledge of Kubernetes (pods, deployments, logs, metrics)
Basic understanding of Apache Spark (jobs, stages, execution lifecycle)