Patras, Western Greece, Greece
For me, everything started at the period when - like almost every teenager - I was founding difficult to choose the best path for my future. Then, a simple idea popped up in my mind; "Follow the General - to - Specific rule". Based on that (and maybe on a natural talent on Mathematics), I soon found myself at the Department of Mathematics of University of Patras. There I became familiar with various paths that I could follow through Mathematics, but the one that attracted my attention the most, was the world of Data, introduced to me as part of my Computational Mathematics specialization. So, immediately after my graduation, I decided to dive in this exciting world, studying for a MSc in Data Science at the Department of Mathematics and spending a lot of free time in order to learn more about it. Next, I started my professional career journey, as a Data Engineer of the Deloitte Consulting team, in Greece! I spent two completely transformative years there, gaining valuable technical and soft skills. I got familiar with the fast paced world of consulting and learnt how to work in a professional environment with high demand. But all things come to an end, so I decided that the time is right to take my next step forward. I decided to switch from the consulting to product side and becoming the "data-guy" at Homelike was the exact opportunity I needed. There, I worked on almost everything related to data, from ETL pipelines to recommendation systems and from scratching my head to fix a small live deployment error to remembering my mathematics background in order to create a better ML model. Unfortunately, Homelike ceased its operations back in 2025, so I had to move on to my next chapter. That was again an about 6-month role in Accenture, working on some big Data ETL pipelines using spark and Kafka. This intense time period led me to grabbing the opportunity to move to Protasis and explore the data behind the Energy sector. My role there involves a wide range of things, including designing AWS-based architecture, building robust, modular and reusable python code to implement event-driven pipelines, becoming familiar with DevOps and software engineering practices and testing my data analysis skills by creating QuickSight dashboards for our clients. This is my day - to - day work now and I am really excited to see what the future brings! I truly believe that I have much more left, as I try to achieve my life's main goal; Constantly getting better in every aspect of it!
Design and implement serverless data pipelines on AWS using Python and Lambda to process client data from S3 into analytical storage layers (S3, RDS). Build event-driven processing architectures triggered by S3 events, using fan-out execution patterns where needed. Develop reusable Python libraries, distributed as internal packages via AWS CodeArtifact and managed using Poetry. Implement CI/CD pipelines using AWS CodePipeline to automate deployment of infrastructure and data pipeline code. Provision and maintain cloud infrastructure using Terraform. Enable local development and testing of AWS pipelines, replicating cloud execution environments using Poetry-based virtual environments. Build analytics datasets and dashboards using Athena and Amazon QuickSight, enabling data insights for client projects. Contribute to the development of internal data engineering practices, including reusable components, deployment workflows and development environments.
I designed and developed big data ETL processes as part of a data mesh architecture. I used Apache Spark and Kafka for scalable streaming data manipulation. I participated to requirements definition and overall architecture implementation.
I developed and deployed data-driven and machine learning systems across a cloud-first infrastructure, mainly using Python and AWS. I worked on projects like: Apartment ranking algorithm using standard data manipulation and ML python modules like pandas and scikit learn Apartment image classification and scoring system using TensorFlow pre trained models Live database update stream feeding AWS Kinesis, triggering multiple workflows End-to-end ETL processes, including batch pipelines from NoSQL databases to a relational data warehouse (Google BigQuery), and real-time data synchronization with Salesforce Predictive analytics tools, such as a request cancellation prediction system, served by Sagemaker endpoints Custom internal APIs for cross-team data access using Flask. All the deployments were streamlined to AWS using the Serverless framework, enabling ECS tasks or Lambda functions, mainly orchestrated mainly by Step Functions, to integrate with either NoSQL MongoDB, Google BigQuery or Salesforce, depending on the specific task. I also used monitoring tools like Grafana, Sentry and Cloudwatch to ensure reliability and performance. During my time here, I collaborated with diverse cross-functional teams in an Agile environment (using tools like Jira), enhancing communication and teamwork skills by engaging people with different backgrounds, from data analysts and product owners, to Salesforce admins and business stakeholders.
I worked in favor of a banking client, owning two of its regulatory reporting projects, designing and implementing the SQL ETL processes that gathered and transformed the Bank's core data into the reporting datasets. I also participated in the preliminary analysis and was ensuring their monthly smooth conduction. Furthermore, I had various tasks regarding error handling of the bank's core procedures. Besides the pure data engineering side, my day - to - day work demanded a lot of business understanding and communication with the relevant stakeholders.