Senior/Staff Full Stack Software Engineer, Frontend Leaning

This role is suitable for a Senior or Staff-level full-stack engineer with 7+ years of experience, particularly someone who is frontend-leaning and highly proficient in React, TypeScript, Python, and SQL. The candidate should be skilled in building modern customer-facing web applications, creating reusable and intuitive UI components, integrating AI-powered development tools, and collaborating closely with Product, Design, and end-users. They should be comfortable owning complex features end to end, translating user needs into solutions, making technical decisions independently, and contributing to both frontend and backend development.

How would you design a React and TypeScript frontend for displaying large volumes of data-quality anomalies and root-cause analysis results?

I would use reusable, strongly typed components with virtualization, pagination, and server-side filtering to keep rendering responsive. I would separate data-fetching state from presentation state and use consistent loading, error, and empty states.

How do you structure a scalable React application with reusable components and predictable state management?

I organize the application by feature boundaries, keeping domain logic close to the features that use it while extracting genuinely reusable UI components. I use local state for isolated interactions and a dedicated server-state solution or shared store only when data must be coordinated across routes or components.

How would you ensure type safety across a TypeScript frontend and Python backend?

I would define explicit API schemas and generate or share types where practical, validating responses at runtime because compile-time types cannot protect against malformed network data. API contracts should be tested in CI and versioned carefully to prevent breaking frontend consumers.

How would you optimize a React interface that becomes slow when users explore complex anomaly visualizations?

I would profile first, then address unnecessary renders through component boundaries, memoization, stable props, and virtualization. For expensive transformations, I would move work off the main thread or pre-aggregate data on the backend, while using progressive loading for large visualizations.

How would you design a Python backend API for anomaly results, filtering, and root-cause analysis?

I would expose resource-oriented endpoints with validated request parameters, consistent response schemas, pagination, and clear error semantics. Filtering and aggregation should be pushed to the data layer when possible, with authorization and observability applied consistently at the API boundary.

How would you write efficient SQL for investigating anomalies across large datasets?

I would first inspect the query plan and ensure filtering, joins, and aggregations can use appropriate indexes or partition pruning. I would select only required columns, avoid unnecessary nested queries, and consider precomputed summaries when the same analytical workload is repeated frequently.

How would you handle asynchronous or long-running root-cause analysis jobs in the web application?

I would submit the job through an idempotent API, return a job identifier, and expose status and result endpoints. The frontend could use polling or server-sent events with retry and cancellation behavior, while the backend tracks progress, failures, and expiration.

How do you test an end-to-end customer-facing feature spanning React, Python, and SQL?

I would combine unit tests for domain logic and components, API or contract tests for frontend-backend integration, and a small number of end-to-end tests covering critical user journeys. Tests should include loading, failure, authorization, empty-data, and large-data scenarios.

How would you improve accessibility and usability when translating product designs into React components?

I would use semantic HTML, keyboard-accessible interactions, visible focus states, correct labels, meaningful error messages, and appropriate ARIA only when native semantics are insufficient. I would validate designs with automated checks and manual keyboard and screen-reader testing.

How do you use AI-powered development tools while maintaining code quality and sound engineering judgment?

I use AI tools for exploration, boilerplate, debugging hypotheses, test generation, and code review assistance, but I verify outputs against requirements, security practices, performance constraints, and existing architecture. Generated code must still pass reviews, tests, static analysis, and appropriate manual validation.

Anomalo is an AI-powered data quality company helping Fortune 500 teams make data trustworthy at scale. As a Senior/Staff Full Stack Software Engineer, you’ll own customer-facing features end to end, translating product ideas into intuitive interfaces while contributing across the backend and partnering closely with Product, Design, and users. The stack includes React, TypeScript, Python, and SQL, with AI-powered development tools encouraged; this is a fully remote US role with approximately 10% travel and a salary of $170K–$220K plus equity. Apply to shape high-impact technical decisions, build modern data products, and join a supportive, values-driven team with generous benefits and meaningful ownership.