I am a Data Engineer dedicated to solving complex data challenges. With over 5 years of experience in data, I specialize in the end-to-end data lifecycle, from ingesting data from various sources to serving modeled data for analytics. My workflow is deeply rooted in engineering best practices, leveraging Terraform for AWS resource provisioning such as Glue, Lambda, and DMS, and utilizing Python to orchestrate data transformations. I also have a strong background in integrating data lakes with visualization tools like Power BI.
Currently, I am focused on refining Lakehouse architecture to improve data accessibility and efficiency. I am fluent in English and have experience working in international environments, which has enhanced my ability to collaborate across diverse teams. My expertise includes building scalable cloud data pipelines on AWS, implementing serverless architectures, and optimizing legacy data processes for better governance and cost reduction.
I am proficient in developing ETL/ELT pipelines, managing workflows with tools like Airflow and CloudWatch, and ensuring data quality and reliability through proactive monitoring. My experience extends to big data analytics using Amazon Athena and modern data engineering tools such as dbt, Spark, and PySpark. I am passionate about delivering high-integrity data solutions that empower business intelligence teams and drive data-driven decision-making.
Throughout my career, I have contributed to various industries including finance, manufacturing, and packaging engineering, where I applied my skills to automate processes, develop KPIs, and create interactive dashboards. I am committed to continuous learning and have earned multiple certifications in cloud computing, data warehousing, and Power BI.
I am eager to bring my technical expertise and problem-solving skills to new challenges, helping organizations harness the power of their data to achieve strategic goals.
