About

I'm a data scientist and developer with an MSc in Data Science from the University of Southern Denmark. I build data pipelines, forecasting models, and automation solutions. Before moving into tech, I spent five years teaching science at a STEM-oriented boarding school. That background gives me a strong ability to communicate complex topics and collaborate across disciplines.

Experience

Data Pipeline Consultant, eulaw.ai (2025-2026)

Solo data engineer building a large-scale data ingestion pipeline for EU and Danish legal documents. Developed 15+ adapters for different institutional sources (REST APIs, SPARQL endpoints, Atom feeds, sitemap crawls, PDF extraction). Multi-layer architecture processing over a million documents. Also contributed to CI/CD, deployment pipelines, and cloud infrastructure. Python, AWS, serverless.

Developer, Flexbuy Technologies (2023-2025)

Full-stack developer in a small company. Worked with backend logic, databases, automation, and data workflows. PHP, MySQL, API integrations, Git.

Science Teacher, Nordborg Slots Efterskole (2018-2023)

Taught maths, physics, and chemistry. Built curricula for programming, game development, web development, and robotics.

Skills

Education

MSc in Business and Economics Data Science, University of Southern Denmark, 2025

Thesis in collaboration with EWII on forecasting changes in the Danish electricity market using deep learning (LSTM, TCN, Transformers) in Databricks.

Teacher Education, University College of Southern Denmark, 2015

Languages

Danish (native), English (professional), German (fluent), Spanish (fluent)

References

Julian van Kranendonk, eulaw.ai

Original (Danish):

Jeg kan hermed varmt anbefale Mario Festersen på baggrund af hans arbejde som Data Engineer i vores virksomhed fra november 2025 til februar 2026.

I sin tid hos os arbejdede Mario med opbygning og vedligeholdelse af en kompleks datapipeline til indsamling, transformation og kvalitetssikring af data fra et stort antal heterogene offentlige datakilder. Arbejdet krævede en bred teknisk profil og evnen til at navigere i mange forskellige API'er, dataformater og systemarkitekturer.

Teknisk bredde og dybde: Mario udviklede selvstændigt dataadaptere til 15+ forskellige datakilder, der hver især havde unikke udfordringer: REST API'er, SPARQL-endpoints, Atom feeds, sitemap-baserede crawls og PDF-ekstraktion. Han mestrede hele spektret fra dataopdagelse og kildeanalyse til produktionsklar implementering med fejlhåndtering og test. Han arbejdede på tværs af en flerlagsarkitektur og tog ansvar for hele flowet, fra rå dataindhentning over struktureret transformation til endelig leveranceklar data. Han demonstrerede stærk forståelse for datamodellering, skemahåndtering og datakvalitetssikring.

Produktivitet og selvstændighed: Over sin ansættelsesperiode på ca. 4 måneder leverede Mario over 430 commits med en jævn kadence, hvilket vidner om høj produktivitet og vedholdenhed. Han håndterede selvstændigt hele onboardingprocessen for nye datakilder, fra indledende kildeanalyse og evaluering til fuldt funktionelle, testede og deployede adaptere.

Infrastruktur og DevOps: Ud over det rene dataarbejde bidrog Mario også til infrastruktur og CI/CD: opsætning af deploymentpipelines, cloud-konfiguration og skabeloner til serverless-arkitektur. Han viste en pragmatisk tilgang til drift og forstod vigtigheden af overvågning, fejlhåndtering og kostnadsoptimering.

Kvaliteter: God forståelse for end-to-end datapipelines i cloud-miljøer. Stærk fejlfindingsevne, mange af hans bidrag adresserer edge cases og subtile datakvalitetsproblemer (tegnkodning, URL-formatering, indholdstypefiltrering). Dokumentationskultur, bidrog med kildeanalyser, arkitekturdokumentation og testplaner.

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English translation:

I can warmly recommend Mario Festersen based on his work as a Data Engineer in our company from November 2025 to February 2026.

During his time with us, Mario worked on building and maintaining a complex data pipeline for collecting, transforming, and quality-assuring data from a large number of heterogeneous public data sources. The work required a broad technical profile and the ability to navigate many different APIs, data formats, and system architectures.

Technical breadth and depth: Mario independently developed data adapters for 15+ different data sources, each with unique challenges: REST APIs, SPARQL endpoints, Atom feeds, sitemap-based crawls, and PDF extraction. He mastered the full spectrum from data discovery and source analysis to production-ready implementation with error handling and testing. He worked across a multi-layer architecture and took responsibility for the entire flow, from raw data collection through structured transformation to final delivery-ready data. He demonstrated a strong understanding of data modelling, schema management, and data quality assurance.

Productivity and independence: Over his employment period of approximately 4 months, Mario delivered over 430 commits at a steady cadence, demonstrating high productivity and persistence. He independently managed the entire onboarding process for new data sources, from initial source analysis and evaluation to fully functional, tested, and deployed adapters.

Infrastructure and DevOps: Beyond the core data work, Mario also contributed to infrastructure and CI/CD: setting up deployment pipelines, cloud configuration, and templates for serverless architecture. He showed a pragmatic approach to operations and understood the importance of monitoring, error handling, and cost optimization.

Qualities: Strong understanding of end-to-end data pipelines in cloud environments. Strong debugging ability. Many of his contributions addressing edge cases and subtle data quality issues (character encoding, URL formatting, content type filtering). Documentation culture. Contributed source analyses, architecture documentation, and test plans.