Million-Data-Point Daily Processing Engine

How we built a system that monitors job listings, news, and content across 11,000 companies

The Situation:

During our startup journey with Unveil, we faced a massive data challenge. We needed to track how companies were growing based on their job postings, monitor department expansions, catch new market entries, and spot fresh C-level hires - all without relying on LinkedIn's limited data.

But job listings were just the beginning. We also needed real-time tracking of company news, blog posts, and media mentions to build a complete intelligence picture.

And the kicker? We needed this to work for virtually ANY company with an online presence - across multiple industries, countries, and data sources.

What Did We Need to Build?

A system that could:

• •Accept any company name and auto-configure itself to that company's digital footprint

• •Process millions of data points daily without breaking a sweat

• •Clean, categorize, and normalize wildly inconsistent data

• •Scale efficiently across thousands of companies

• •Deliver actionable intelligence, not just raw data

How Did We Build It?

The Approach

1. .Created an intelligent configuration layer that could analyze a company's online presence

1. .Built adaptive data-fetching mechanisms that work across different site structures

1. .Developed advanced cleaning algorithms to handle inconsistent data formats

1. .Implemented categorization models to sort content by relevance and type

1. .Engineered a processing pipeline that could handle massive scale

The Technical Stack

We created a multi-layered system:

• •Front layer: Accepts company name, generates full configuration profile

• •Processing layer: Cleans, categorizes, and normalizes inconsistent data

• •Scaling layer: Manages distributed processing across thousands of data sources

The Results? Massive Scale

• •Processing 1.2 MILLION data points daily

• •Monitoring 11,000 companies simultaneously

• •Complete company setup with just a company name

• •Real-time intelligence across jobs, news, and company content

What Did We Learn?

1. The challenges:

• •Companies have wildly different digital footprints

• •Data consistency is non-existent across sources

• •Processing at scale requires serious architecture planning

2. Technical insights:

• •Auto-configuration is possible but requires sophisticated pattern recognition

• •Data normalization is the hardest part of the pipeline

• •Categorization requires both rules-based and ML approaches

• •Scale requires thinking differently about architecture

The Bottom Line

While our startup ultimately faced product-market fit challenges, this system demonstrated our ability to build sophisticated, high-scale data processing systems that can handle messy real-world data.

The technical achievement - processing 1.2 million data points daily across 11,000 companies with just a company name as input - shows our capacity to solve complex data challenges at massive scale.