Vellinge, Sweden
+46 733017732 | artmro@gmail.com | GitHub: afuyo

Senior data architect and data engineering with 15+ years of experience designing source-of-truth data platforms, semantic data models, and operational analytics solutions across insurance, finance, and pharma.

Strong background in analytics strategy, data product platforms, metadata-driven automation, event-driven analytics, and cross-functional collaboration between business and engineering teams.

I specialize in turning complex business processes into governed analytical platforms that improve metric consistency, operational visibility, and decision support.

• Analytics strategy and operational intelligence
• Data product platforms and enablement
• Semantic and canonical data modeling
• Unified business metrics and governed definitions
• Source-of-truth data platforms
• Event-driven analytics and operational modeling
• Metadata-driven automation
• Domain-driven design and Event Modeling
• Data governance, lineage, and metadata management
• Snowflake, SQLMesh, dbt, SQLGlot
• Kafka, GraphQL, Neo4j
• AWS and Azure

Oct 2023 – Present

• Developed a revenue intelligence platform enabling standardized business metrics including NDR, GRR, Logo Retention, and funnel analytics across domains.
• Built an event-driven analytical model enabling drill-down from executive KPIs to individual business events and operational decision points.
• Established reusable semantic definitions and time-series business events to improve metric consistency, explainability, and cross-functional trust in analytics.
• Enabled analysts and business stakeholders to trace revenue movements and customer lifecycle changes through sequenced business activities rather than isolated snapshots.
• Created a foundation for future context-aware analytics and richer operational intelligence capabilities.

• Helped shape a data product strategy focused on reducing fragmentation, improving interoperability, and enabling scalable analytics delivery.
• Built a shared data product platform improving consistency, reuse, and delivery speed across analytical domains.
• Replaced fragmented self-service patterns with reusable governed building blocks and shared semantic concepts aligned with ACORD and domain-driven design principles.
• Replaced dbt with SQLMesh to improve scalability, maintainability, and developer workflow for analytical data products.
• Introduced isolated virtual development environments reducing dependency conflicts and improving collaboration across teams.

• Designed a metadata-driven model generation approach turning declarative YAML definitions into governed SQLMesh models.
• Automated generation of analytical building blocks including hook, bridge, and event models from reusable metadata, reducing manual SQL development and improving structural consistency across domains.
• Modeled temporal validity, dependency-aware bridge logic, and reusable business keys as metadata instead of hand-coded model patterns.
• Used dependency graphs and topological ordering to generate analytical structures in the correct sequence, improving repeatability and reducing delivery risk.

Sep 2021 – Sep 2023

• Defined a common data layer strategy reducing fragmentation and improving interoperability across analytics and operational domains.
• Replaced a monolithic data lake approach with a flexible platform inspired by Data Mesh principles.
• Enabled scalable self-service analytics while maintaining governance and semantic consistency.
• Collaborated across technical and business domains to align analytical capabilities with organizational needs.

Technologies: Snowflake, dbt, AWS

Aug 2016 – Aug 2021

• Built an underwriting data platform replacing Excel-based workflows with centralized governed data flows and shared business logic.
• Introduced Event Modeling to improve collaboration between business and engineering stakeholders.
• Designed enterprise semantic and canonical models based on ACORD to standardize concepts and improve interoperability across domains.
• Built event-driven write-back capabilities using GraphQL and Kafka, enabling applications to publish operational outcomes back into the analytical ecosystem.
• Automated logical and physical model generation using metadata-driven techniques, improving consistency and reducing manual effort.

• Developed a semantic data platform enabling standardized interchange of business information across departments.
• Implemented graph-based semantic models in Neo4j aligned with the ACORD insurance ontology.
• Exposed semantic models through GraphQL APIs and integrated event streams using Kafka.
• Improved consistency and discoverability of enterprise data assets.

• Implemented metadata-driven lineage and business glossary capabilities using semantic technologies and Neo4j.
• Exposed metadata and lineage through Amundsen data catalog.
• Improved discoverability, governance, and understanding of analytical assets across teams.

Nov 2015 – Aug 2016

• Designed target architecture and business proposal for a large-scale MS SQL Server data warehouse initiative.
• Participated in Data Governance Board activities defining enterprise data standards, policies, and governance principles.

Aug 2014 – Nov 2015

• Led evaluation and proof of concept for data warehouse automation tooling using WhereScape RED.
• Improved reliability and reduced time-to-market for BI deliveries through automation and streamlined delivery workflows.

• Swedish Agency for Marine and Water Management — BI Consultant (2013 – 2014)
• DR — Business Intelligence Developer (2012 – 2013)
• Danske Bank — External Consultant (2010 – 2012)
• SimCorp — Software Developer (2008 – 2010)
• Copenhagen Energy — DBA (2004 – 2008)
• Quibus International AB — Software Developer (2002 – 2004)

• Certified Scrum Product Owner (2022)
• Data Mesh — Domain Oriented Data (2022)
• Lead Enterprise Architecture Program on Azure — Microsoft (2017)
• Data Modelling Master Class — Steve Hoberman (2016)
• Data Vault Certified Data Modeler (DVCDM) (2014)
• CDMP Preparation (2024)

• Knowledge Graph Academy ongoing
• Claude Academy Claude Certified Architect track (ongoing)

Faculty of Informatics

Key areas: Database systems, data modeling, software architecture, UML, object-oriented design, Java programming, algorithms, and data structures.

• Apache Spark Workshop for Developers
• Distributed Computing with Spark
• Data Science Specialization — Johns Hopkins University
• Statistical Analysis with R
• Oracle BI EE
• C++ Programming
• SQL Server Administration
• Oracle Administration

• Democratizing Data — Tryg and Prisma (2021)

• Building Semantic Data Hub with LPG, GraphQL and Kafka Streams (2019)

• GraphQL & Graph Data Modeling in Neo4j

• Azure Functions & Machine Learning

• XML Parser for Danish Motor Register Data