I am a Data and Business Intelligence Engineer with more than three years of experience building production data pipelines, data warehouses, and analytics platforms. I have worked across banking, insurance, maritime, and telecommunications environments, translating business requirements into reliable, decision-ready data products.
My core technical strengths are Python, PySpark, SQL, T-SQL, Apache Spark, and the Microsoft data ecosystem. I develop ETL/ELT processes, dimensional models, data marts, and reporting solutions using platforms including Microsoft Fabric, Databricks, SQL Server, Power BI, SSIS, SSAS, and SSRS.
In my current role, I build and support cloud data warehouse and lakehouse solutions, develop Spark-based transformations, and deliver curated Delta tables for analytics. I also administer Fabric workspaces and deployments, build Power BI reporting, and optimize data pipelines through techniques such as partitioning, caching, broadcast joins, and incremental loading.
I have experience designing enterprise data warehouses and star schemas, maintaining historized datasets, and automating nightly data loads. I am comfortable orchestrating workflows with Airflow, using AWS S3 for landing storage, and supporting reporting and data-quality issues across both on-premises and cloud environments.
Earlier in my career, I developed SQL-based data solutions and ERP reporting for the maritime sector, including .NET, C#, VB.NET, and PowerShell development. My professional foundation also includes banking operations and customer support, which helps me understand business processes and communicate effectively with technical and non-technical stakeholders.
I hold a BSc in Economics and completed an Advanced Data Analytics Bootcamp focused on the end-to-end analytics lifecycle. I am motivated by building scalable, well-documented data platforms that improve data quality, automate operations, and enable better business decisions.
