Copenhagen, Capital Region of Denmark, Denmark
Exceptional human and collaboration skills. Intellectual curiosity and desire to learn new things, techniques and technologies.
At Lunar, I build and maintain scalable data infrastructure to power analytics, machine learning, and operational tools across the organization. I’ve developed and deployed microservices in Go and Scala for ingesting data from source systems to the datalake, supporting use cases from batch analytics to real-time streaming. I’ve designed and managed Terraform infrastructure in AWS, contributed to the evolution of our RBAC-based access control, and led the creation of CI/CD pipelines for our data products. I played a key role in deploying Kubernetes workloads for internal data platforms, including a self-service operational insights tool. I also supported ML processes by enabling infrastructure and feature store use cases, and led the design of a GDPR-compliant data cleaner for early redaction of sensitive data. Additionally, I configured and deployed Apache Iceberg, contributing to modern data lake architecture, and helped improve streaming platform workflows to ensure robust, scalable real-time data ingestion.
At 9FWR, I built and maintained production-grade data pipelines in a GCP environment using Python, Airflow, and BigQuery. I contributed to real-time ingestion workflows and developed ELT pipelines across multiple internal systems. I also worked on CI/CD automation, infrastructure-as-code with Terraform, and deployed containerized services using Docker and Kubernetes. Additionally, I helped prepare and clean datasets for analytics and early machine learning use cases, supporting downstream data consumers with reliable, high-quality data.
As an official GCP technical support representative, I helped global clients troubleshoot and resolve complex issues across core Google Cloud data products, including BigQuery, Dataflow, Pub/Sub, Composer, Dataproc and BigTable. I diagnosed and reproduced production errors, advised on performance tuning, and resolved infrastructure failures related to data processing, orchestration, and pipeline reliability. This role deepened my expertise in GCP's data ecosystem and strengthened my ability to solve real-world distributed system challenges under pressure.
As a Data Engineer at Everis, I built real-time analytics pipelines using Apache Spark, enabling the business to process large-scale datasets and generate timely insights. I supported early ML initiatives by preparing structured datasets and collaborating with data scientists on feature extraction. I also developed data integration workflows and created interactive dashboards to support business intelligence projects across departments.
Selected as part of a high-performance student teaching program, I delivered reinforcement lessons in Computer Science and Calculus to first-year engineering students. I also supported strategic initiatives at the Barcelona School of Industrial Engineering and helped modernize teaching materials by updating and adapting manuals to better fit student needs. This experience sharpened my ability to explain complex technical concepts clearly, a skill I continue to apply in collaborative, cross-functional engineering environments.