Tom Kral

Data Engineer and Data Enthusiast

I focus on creating clear and maintainable data solutions that help organisations work with their information more effectively. Outside of work, I like exploring practical ways to collect, process and understand data from the world around me.

I work as a data engineer, mainly on ELT pipelines and structured data workflows. I started out experimenting with big data tools and machine learning, and that curiosity has stayed with me. At home, I often find myself setting up small experiments, automating something for convenience, or analysing data from sensors or nature. I enjoy projects that help me understand how things behave and how data can make that a bit clearer.

Skills & Tech

I work with a range of tools and techniques that support both my professional work as a data engineer and the projects I explore at home. These skills help me build reliable data pipelines, analyse information and create small systems that make tasks easier or more insightful. Below is an overview of the technologies I use regularly and continue to develop through practical experience.

Python

Python is the language I reach for first when working on data problems. I use it for data processing, automation and building tools that help me understand how things behave. Python's many libraries for data fit well with the way I like to work.

ELT pipelines

Building ELT pipelines is a large part of my work as a data engineer. I enjoy creating processes that take raw information and make it reliable for analysis. A good pipeline is predictable and easy to understand, and I aim for that in everything I build. It is satisfying work because it forms the foundation on which others can build.

SQL

SQL is the language I use every day to work with databases and manage structured data. It helps me keep information organised and easy to work with. I enjoy writing queries that are clear, efficient and easy to maintain.

Data Warehousing

I work on designing and maintaining data warehouses that support long term use and clear data models. My goal is to create structures that are easy to navigate and continue to make sense as a system grows. I focus on stability and clarity so that the data remains useful over time. A well organised warehouse makes working with data much more enjoyable.

Machine Learning

Machine learning is something I continue to explore in my own time. I have worked on a few projects that apply models to real data and help uncover interesting patterns. It is a field that keeps challenging me, and I enjoy learning more about it step by step. Even small experiments can lead to good insights.

Home Projects and Experiments

Many of my nature related data projects are small, personal experiments that let me test new techniques. They often start with a simple question and grow into useful workflows that deepen my understanding of both data and the environment it describes.