Data Analysis | Analytics Engineer

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I am a Freelance Analytics Engineer focused on transforming raw data into reliable, actionable datasets. I work with SQL, Python, and modern data workflows to collect, clean, validate, and integrate information from multiple sources.

My experience includes data extraction, transformation, quality assurance, and process automation. I enjoy building solutions that reduce manual work, improve data consistency, and help organizations make decisions based on accurate information.

Whether working with databases, CSV files, APIs, or reporting layers, I focus on creating efficient and maintainable data processes that support analytics and business operations.

What I work with

Tools I'm working with: Python / Pandas, Power Query, SQL (MySQL, PostgreSQL)

Data ckeaning & preparation - turning scattered information into predictable, structured datasets.
Master Data organisation — naming rules, categories, hierarchies, consistency, clarity.
Data standardisation — making information behave the same way across systems and teams.
Process simplification — removing noise, reducing friction, making everyday work calmer.
SQL & Python workflows — simple, readable scripts that don’t break when data changes.
Documentation — clear, human‑readable explanations that survive onboarding and turnover.
GDPR‑aligned data handling — safe, predictable, compliant processes.

How I Work?

A clear, predictable workflow focused on clean, reliable data foundations.

Review your data sources I look at the structure, quality and purpose of your data to understand what you need.
Prepare a clean, reliable dataset I clean, organise and standardise your data so it’s ready for reporting and analysis.
Build clear, stable data foundations Instead of dashboards or visual layers, I focus on logic, naming rules, Master Data, deduplication and processes that don’t break when data changes.
Provide documentation and handover You receive documentation, explanations and all files needed to continue using the solution independently.

My approach

I work with data the way a craftsperson works with materials: calmly, precisely and with respect for structure. I don’t rush to visualise or decorate information. I focus on foundations — naming rules, consistency, logic, predictable behaviour. Good data should feel quiet, stable and easy to work with.

Problem & Solve

Data that behaves differently in every file or system. Inconsistent naming, categories or structures. Multiple versions of the same information. Processes that break when data changes. Datasets that nobody fully trusts. Documentation that doesn’t survive onboarding.

My Data Principles

I treat data as a material — something that needs care, structure and patience. Not a product to decorate, not a dashboard to impress, but a foundation that supports everyday work. I believe that good data feels quiet. It doesn’t demand attention, doesn’t surprise, doesn’t create friction. It behaves the same way today, tomorrow and next quarter. I value clarity over complexity. Simple naming, consistent categories and predictable logic make teams faster than any visual layer. I work with data in a way that respects how people actually use it. Processes should reduce stress, not add more of it. Documentation should survive onboarding and turnover. Files should make sense even months later. I build data foundations that help organisations breathe easier. Calm, stable, understandable — ready for whatever comes next.