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CASE STUDY

Recruitment Data Platform for Hiring and Talent Analytics

INDUSTRY

Retail & Consumer

USE CASES

Data Platform Pipeline Analytics

BUSINESS IMPACT

Efficiency Improvement

TECHNOLOGIES

Apache Spark AWS Azure Data pipelines (ETL/ELT) Data visualization tools Databricks Python

Client Overview

Industry

Retail & Consumer

Region

UK & Europe

Company Size

Mid-market

Profile

Recruitment-focused organization managing candidate pipelines, hiring workflows, and performance tracking across multiple roles and clients

Project Background

The client required better visibility into hiring processes and more structured data to support decision-making.

The challenge

Recruitment data was spread across multiple tools and workflows, including ATS systems, spreadsheets, and internal tracking processes.

This resulted in:

  • Limited visibility into pipeline performance and bottlenecks
  • Inconsistent data across hiring processes
  • Time-consuming manual reporting
  • Difficulty measuring performance across roles, teams, and clients

There was a need for a centralized system to structure data and support better hiring decisions.

What we built

We designed and implemented a recruitment data platform to unify hiring data and enable consistent analytics.

The solution included:

  • Data pipelines integrating data from ATS systems and internal sources
  • A centralized data layer combining candidate, role, and process data
  • Structured reporting and analytics capabilities
  • Dashboards supporting pipeline visibility and performance tracking

The platform was designed to provide a consistent view across hiring workflows and support ongoing analysis.

Impact in practice

The implementation enabled a more data-driven and responsive production environment:

Improved visibility into hiring pipelines

by consolidating data across roles, stages, and teams

More consistent and reliable reporting

through structured data and standardized metrics

Reduced manual reporting effort

by automating data collection and aggregation

Better understanding of process performance

through clear insights into conversion rates and bottlenecks

Technologies used

Several technologies were used in order to deliver this project for the client, among which:

Data pipelines (ETL/ELT)
Data visualization tools
Python

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