LINKEDIN JOBTRACKER DASHBOARD

[ Personal Project ]

About

JobTracker is a fictional dashboard app designed within the LinkedIn ecosystem as a personal UX/UI case study. The project focused on creating a clean and intuitive experience for tracking job applications, interviews, and career opportunities, while staying consistent with LinkedIn’s visual language and design system. It was also an opportunity to explore advanced prototyping and interface design workflows in Figma, showcasing my approach to dashboard design, information hierarchy, and AI-assisted UI development.

The challenge

Looking for a job can be quite a mess. Thankfully, LinkedIn already provides many of the individual pieces of the job-search experience, but I wanted to explore what would happen if these pieces were brought together into a dedicated job-search dashboard.
The central question became: how can we turn a collection of job applications into a clear picture of the user's overall job search?
This led to three design goals:

[1] Create visibility: Give users an immediate overview of their current applications and opportunities.

[2] Reduce cognitive load: Make it easy to understand what is happening across multiple applications without opening each one individually.

[3] Turn data into insight: Go beyond simply listing applications and help users identify patterns in their job search.

JobTracker dashboard, with stats describing the current applications and the requested skills.

Key UX decisions

Prioritise the overview

The dashboard's first job is to answer: "How is my job search going?"
I therefore prioritised high-level information before individual application details. The user can understand the overall state of their search at a glance, then progressively drill down into the details.
This follows a simple hierarchy of overview > pattern > detail, rather than presenting every piece of information with equal importance.

Use visualisation where it adds meaning

A dashboard doesn't automatically become useful because it contains charts. Every visualisation should answer a question.
The purpose of the visual elements is therefore not decoration, but reducing the amount of interpretation required from the user.

Make the data actionable

If the dashboard shows that a particular skill appears frequently across the user's target jobs, that information should suggest an action: should I improve this skill?
Or: most applications are still at the initial stage; should I review my CV or targeting strategy?
The longer-term idea is for JobTracker to evolve from a passive record of applications into a tool that helps users make better decisions about their job search.

Keep the LinkedIn ecosystem familiar

Because JobTracker is imagined as part of LinkedIn, I deliberately kept the visual language close to the existing ecosystem. The objective wasn't to invent a completely new visual identity. Instead, I wanted the experience to feel like a natural extension of a product users already understand. This influenced typography, color usage, component structure and interaction patterns. That constraint became useful from a UX perspective: the design could focus on introducing a new workflow without simultaneously asking users to learn a completely new visual language.

Detail on the hover design for a dashboard component.

A retrospective

JobTracker was primarily an exercise in information hierarchy. The challenge wasn't finding enough things to put on the dashboard: it was deciding what deserves to be seen first. A dashboard can very easily become a collection of charts, numbers and cards. The more interesting design problem is understanding what decisions the user needs to make and structuring the information around those decisions. This project reinforced my belief that good dashboard design is less about data density and more about progressive disclosure.

As a next step, because this is a fictional product concept, I would look for validation. I'd want to understand whether users actually find the aggregated information useful, particularly the skills analysis and higher-level application statistics.
I'd test questions such as:

• Which information do users look for first?
• Do the dashboard metrics actually help them make decisions?
• Which visualisations are useful and which are simply interesting?
• Does seeing aggregated skill requirements change how users approach their applications?
• Would users trust LinkedIn to interpret their job-search data in this way?