Ferway

Designing data import and job matching workflows

Project overview

Ferway is a compensation management platform that helps companies make data-driven salary decisions through market benchmarks, internal salary analysis, and variable compensation management.

To deliver accurate and actionable insights, Ferway relies on large volumes of HR data coming from sources such as DSN imports or manual employee inputs.
However, raw data alone doesn’t create value — it must be correctly imported, structured, and aligned with Ferway’s job and market models.

This case study presents two complementary initiatives focused on data activation:
improving the DSN import flow for salary analysis, and designing a job-matching experience to ensure data consistency and reliable market comparisons at scale.

My role
Product Designer (UX & UI)

Context
Internal SaaS product (B2B, HR & Compensation)

Focus
Data onboarding, complex workflows, product iteration, interaction design

Project 1 — DSN import flow (V1)

Problem

Ferway allows users to analyze salaries either by manually creating employee profiles or by importing data in bulk.
While the manual approach is possible, it quickly becomes time-consuming and inefficient as teams grow.

To address this, Ferway introduced DSN imports as a faster way to retrieve employee data automatically — creating employee profiles, positions, and salary-related information in seconds instead of hours.

The challenge was therefore to design a clear and reliable DSN import flow that would encourage adoption while handling complex file formats and validations.

Solution — First version (V1)

The first version of the DSN import flow focused on making bulk data import accessible and trustworthy, while keeping the experience understandable for non-technical users.

The flow was designed as a modal-based experience and included:

Outcome & limitations

The V1 flow successfully fulfilled its core purpose:
reducing manual effort by allowing bulk employee data import through DSN files.

However, early usage revealed several limitations:

• The flow involved multiple modal states, adding friction

• Processing feedback could feel slow or unclear

• The experience prioritized robustness over speed and simplicity

These observations led to a second iteration focused on streamlining the flow, reducing friction, and improving perceived performance, resulting in a redesigned V2.

Solution — Second version (V2)

The DSN import flow was redesigned to reduce friction and better reflect the system’s technical behavior, making file upload faster, clearer, and more intuitive :

Why this V2 works better ?

• Fewer steps, no modal friction

• Clear system feedback at every stage

• Better alignment with technical realities

• Faster perceived performance and higher confidence

Project 2 — Job matching for reliable market comparisons

Problem

As companies grow, job titles become increasingly inconsistent.
Similar roles can be named differently across teams, making salary analysis unreliable.

For Ferway, this created several challenges:

• Company job titles were too heterogeneous to be analyzed directly

• Manual job mapping was time-consuming and error-prone

• Users didn’t always understand which Ferway role to select

• Incorrect mappings led to inaccurate salary benchmarks

As a result, users struggled to fully leverage Ferway’s market analysis capabilities.

Solution

To solve this, I designed a dedicated matching flow that helps users associate their internal job titles with Ferway’s standardized roles, ensuring accurate salary analysis while reducing manual effort.

Impact & learnings

Impact

These two project improved how data is activated within Ferway, making salary analysis faster, more reliable, and easier to scale.

By simplifying DSN imports and standardizing job matching:

• Users can populate employee data significantly faster than with manual input

• Job titles are consistently aligned with Ferway’s market taxonomy

• Salary insights become more accurate and comparable

• Complex setup steps are easier to understand and complete

Key learnings

• Raw data only creates value once it is properly structured and aligned

• Automation should accelerate workflows without removing user control

• Clear system feedback is essential in data-heavy experiences

• Iteration is key when designing workflows with strong technical constraints

• Trust and clarity matter more than speed in critical setup moments

Final reflection

This project highlighted the importance of designing data activation workflows, not just interfaces.

By focusing on clarity, progressive automation, and user confidence, complex HR data can be transformed into reliable and actionable insights, supporting both day-to-day operations and long-term product scalability.