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Royal Blue

Made by Royal Birkdale

ain’t no birkdale, we blue

The purpose of this repository is to build an entire ETL (Extract, Load, Transform) data pipeline in AWS (Amazon Web Services). Extracting data from an OLTP (Online Transaction Processing Database) PostgreSQL database and loading it into an OLAP (Online Analytical Processing Database) database. The data is transformed from transactional day to day business data into a Data Analisys ready format, suitable for multiple Business Inteligence purposes. It uses Python as the main programming language, followed by Terraform for infrastructure as code. It also uses Bash and SQL Scripts to help with build processes and integration testing, and has a full-featured Makefile for convenience.

The Team

San Fernandes

San Fernandes

Data engineer with a passion for streamlining end to end

pipelines using an array of tools such as Terraforn, SQL, AWS and Python.

Janette Samuels

Janette Samuels

I’m a junior data engineer passionate about turning messy,

fragmented data into structured systems that support better thinking, decision-making, and innovation. I bring curiosity, clarity, and care to everything I build; from ETL pipelines to cloud automation. My background in law and sociology sharpened how I approach structure, logic, and systems. Now, I apply those skills through code; building tools that transform raw data into something meaningful, testable, and scalable.

Theo Ribeiro

Theo Ribeiro

Throughout my professional life, I have been especially

interested in five things: organising data, learning, finding the right tool for the job, storytelling and aesthetics. To satisfy the first two, I created databases to make my work more efficient and tried to learn everything I could to deliver the highest quality possible. For the last three, I have made films professionally, constantly searching for the tools that could help me tell better visual stories. Now, while changing careers, I am currently attending a full-time Data Engineering bootcamp at Northcoders after having spent the previous year intensively upskilling in front-end technologies. These have provided me with a solid foundation in Data, Cloud, backend engineering and a thorough understanding of how the web works and how backend and front end integrate and work together. I am super excited to join teams building high-quality products with well-architected solutions that solve both business and customer issues, focusing on readable, tested, scalable and maintainable code.

Charley Bolton

Charley Bolton

I’m a Junior Data Engineer with a background that spans

science, sales and music. Very different spaces, but all of them shaped how I think and approach problems. I studied Chemistry at university, with research focused on quantum computing. It taught me to work through complexity, recognise patterns and approach challenges with structure and curiosity. All of that translates naturally into working with data. After graduating, I worked in sales where I gained a strong understanding of business needs and how to communicate clearly with different types of people. It showed me how valuable it is to listen, adapt and build trust. These are skills I’ve carried into every role since. Alongside that, I’ve spent the past few years immersed in music. I perform as a DJ and organise events across Europe. It’s a creative outlet I care deeply about and it has helped me build intuition and stay calm under pressure. It also keeps me energised and connected to people. I’m currently developing my technical skills through a Python-based course at Northcoders, using tools like SQL, AWS and Terraform to build modern data systems. I’m motivated by turning raw data into something useful and impactful. It’s the kind of work that takes real thought and care behind the scenes. Over time I want to grow in this space, work with people who inspire me and contribute to projects with lasting impact. I’m open to where that leads, but I know I want to keep building meaningful tools and growing as a thoughtful engineer.

Olliver Kwasny

Olliver Kwasny

I’m an Data Engineer and Software Developer with 7 years of

programming experience and a recent graduate of the Northcoders Data Engineering Bootcamp (2025) I’m fast to learn, self-motivated, and eager to contribute to innovative engineering teams. I’m open to roles in Data Engineering, Software Engineering, and Blockchain Development. Always keen to explore new technologies and collaborate on impactful projects.

Tech Stack

Tech Stack for this group

We used Python 3.13.3+ Core Python Dependencies psycopg3 (PostgreSQL database adapter) boto3 for AWS SDK integration pandas for data manipulation pyarrow for Parquet file handling orjson (fast JSON serialisation/deserialization) Development Dependencies pytest and related plugins for testing and coverage ruff for linting and formatting moto for AWS service mocking during tests bandit for vulnerability and security scanning of source code ipykernel for VS Code Jupyter notebook support Databases PostgreSQL (used locally for integration tests, and being the database on both sides of the pipeline). AWS Lambda, S3, Step Functions, IAM, Cloudwatch, SNS Email alerts, etc. All accessed using boto3 deployed with Terraform. Utilities & Tooling uv for managing Python environments, dependencies, and running scripts Makefile for task automation (testing, linting, formatting, deployment). Local Testing Scripts Bash scripts to run SQL test files against the local PostgreSQL database and capture output for validation. Some tools such as uv and ruff we chose because we wanted to experiment with modern tooling for python. Other tools were the industry standards and we wanted to ensure we could effectively utilise them; an example of this was our choice to use psycopg over pg8000, coupled with pandas for data wrangling/transformation.

Challenges Faced

Project management and planning introduced instances were code needed extra attention due to lack of direction, though this was rectified through our regularly held stand up meetings.