About Me
Mukesh Shirke
My personal story - Oil to Data
From High-Pressure Wellbores to High-Value Data Pipelines
I did not plan to become a data analyst. But looking back, the thread was always there.
I started at Halliburton as a Well Design Engineer — designing complex well trajectories, running engineering simulations, and building drilling programmes for onshore and offshore projects. I also rolled out new drilling software across field locations and trained engineers on it, which taught me early that the gap between what a model predicts and what actually happens in the field is where most problems hide.
A few years in I moved into field execution as a Senior Directional Driller. The job was essentially real-time data analysis under pressure — downhole survey data arriving every 28.5 metres, engineering curves telling you what the formation is doing, and signals flagging problems before they become failures. You learn very quickly to read data fast, challenge anything that does not reconcile with the model, and make decisions you can fully defend.
Fifteen years of that shapes how you look at data permanently.
The Strategic Shift — Building the Modern Toolkit
When I relocated to the UK in 2024 I made a deliberate decision to formalise that instinct into modern analytical skills. I systematically worked through the full data stack — SQL, Python, Power BI, Tableau, and cloud data pipelines — not just as courses but through real projects with real datasets and real outputs.
My portfolio spans healthcare performance analysis, retail operations dashboards, cloud ELT pipelines, and data normalisation projects — each one built end to end, from raw data through to strategic recommendations.
Where Domain Expertise Meets Data Science
The project that best captures this intersection is the Autonomous Drilling Console — a working simulation I built entirely from scratch in Python, combining physics-based directional drilling algorithms with an adaptive machine learning engine. Every formula came directly from field experience. It achieved 95% autonomous control across 541 metres and took 20+ development iterations to get right.
It is the most personal thing in my portfolio — because it only works if the physics is right, and the physics is only right if you have actually done the job.
Why This Combination Matters
Most analysts approach a dataset as a technical puzzle. I approach it as an operational reality — because I have spent 15 years on the other side of the report.
I understand what a project manager actually needs from a variance analysis, why a cost deviation matters beyond the numbers, and how to frame an insight so it drives a decision rather than a conversation.
I bring operational maturity, a proven modern data toolkit, and genuine domain depth — a combination that is rare and that I believe makes a real difference in the right role.
Why This Combination Matters
I am actively seeking Data Analyst, Operations Analyst, or Business Analyst roles where that combination is genuinely valued. If that sounds like your kind of person, I would love to connect.