Analytics

Data Analytics

Dashboards, ETL, and BI to turn raw data into decisions.

What's included

  • Dashboard development
  • ETL pipelines
  • Data warehouse setup
  • Real-time analytics
  • Custom reports
  • KPI tracking
  • Integration with existing tools
Data Analytics — service offered by Dharmendra Singh Yadav

Data That Tells You Something Useful

Most companies have more data than they know what to do with. The problem isn't usually a lack of data — it's that the data is scattered across different systems, hard to query, and not connected to the decisions that matter. I build analytics systems that make the right information easy to find and act on.

Dashboards

I build dashboards that people actually use — not dashboards that look impressive in a demo but never get opened. That means understanding which metrics actually drive decisions for your team, designing for the way people interact with data in practice, and building something that loads fast and stays accurate.

ETL Pipelines

If your data lives in five different systems, you need pipelines that pull it together, clean it, and put it somewhere you can query efficiently. I build ETL pipelines that run reliably, handle edge cases, and alert you when something goes wrong rather than silently producing wrong numbers.

Data Warehouse

For analytics at scale, a proper data warehouse setup makes queries fast and cheap. I set this up on BigQuery, Snowflake, or PostgreSQL depending on your volume and existing stack.

Training Your Team

Analytics is most valuable when your team can explore the data themselves rather than submitting requests for every new question. I build self-service capabilities and train your team on how to use them.

Frequently asked

A dashboard shows you what happened. Analytics tells you why, and what to do about it. Most teams have plenty of the first and very little of the second.

Not until querying your production database for reports starts affecting the application, or until you need to join data from several systems. Before that, well-built reporting queries against a read replica are sufficient.

Usually yes, by connecting to the databases and APIs you already have. Building the pipeline first and asking what to measure afterwards is the wrong order and a common source of wasted effort.

Collect what you need and no more, anonymise where possible, set retention limits, and keep personal data out of third-party analytics tools that were never designed to hold it.

Let's talk.

Building production-grade SaaS, AI agents and mobile apps end-to-end.

Hiring for a senior role or have an interesting problem to solve? Drop a note — I read every message.