Frontend Foundation for DataIAm

Snapshot

Role: Frontend Engineer

Sanitized: Yes

Context

DataIAm targets Salesforce administrators who need to clean inconsistent operational data using AI-assisted workflows.

Challenge

The product needed a maintainable UI architecture while the team was shipping critical features on short timelines.

Constraints

• Rapid product iteration cycle with limited delivery window.
• Need for dependable frontend patterns around forms and async state.
• Feature scope required immediate business impact, not long refactor cycles.

Architecture

A focused React architecture centered on predictable state management and robust form workflows for data-cleaning operations.

Frontend

• React + TypeScript foundation with reusable feature modules.
• React Query for async server-state consistency.
• React Hook Form for high-signal data-entry interactions.

Backend

• UI integration against existing AI-backed cleansing workflows.

Integrations

• Salesforce-centric data cleanup flows exposed through product APIs.

Data Flow

• User-provided records move through validation and cleansing actions.
• Results are surfaced with explicit UI feedback for review and iteration.

Execution

Responsibilities

• Defined frontend architecture and implementation direction.
• Led transition from a Next.js-oriented setup to a plain React application structure.
• Delivered major features that were critical to product usability.

Delivery

• Established architecture early to unblock fast feature delivery.
• Prioritized production-critical features without compromising maintainability.
• Created a base the team could keep extending after initial delivery.

Outcomes

• Stabilized frontend direction during a high-pressure build phase.
• Improved implementation speed on critical user-facing features.
• Provided a clearer foundation for ongoing product development.

Leadership

• Made architecture decisions that matched delivery urgency.
• Combined technical depth with clear prioritization in a short engagement.
• Drove high-impact execution in a narrow timeline.

Lessons

Lessons are intentionally omitted in the public sanitized narrative.