Running Signals
A live analytics engineering project that transforms personal Garmin data into explainable consistency, volume, and fitness signals through an end-to-end lakehouse pipeline.
- Python
- Databricks
- dbt
- SQL
- Next.js
Data engineering · Business intelligence · Web
Frank
Stevens
I build reliable data systems and thoughtful digital products.
Data engineer and BI specialist working across pipelines, analytics, backend services, and production web applications.
01
Currently working on
beatledger
A mobile-first running app that turns FIT activity data into training insights and flexible plans, with an integrated data-grounded agent.
- React Native
- Expo
- TypeScript
- FastAPI
- Supabase
- DuckDB
- DeepSeek
02
Working stack
Selected tools and patterns from a broader working stack, spanning data systems, analytics, and product delivery.
Data engineering
- Python · PySpark
- SQL · dbt
- Databricks · BigQuery
- Medallion architecture · ELT
Analytics & BI
- Semantic & dimensional modelling
- Metabase
- Looker
- Power BI · DAX · Power Query
Web & product engineering
- Next.js · React
- TypeScript
- Tailwind CSS
- FastAPI · REST APIs
Cloud & delivery
- GCP · Azure · AWS
- Docker
- Terraform
- GitHub Actions · CI/CD
03
Built as one system.
Strong products do not treat data, backend services, and user interfaces as disconnected concerns. I work across those boundaries while keeping ownership and architecture explicit.
Data platforms
Reliable ingestion, transformation, orchestration, modelling, and observability across modern cloud data stacks.
Analytics and BI
Clear semantic models, durable reporting layers, and decision-ready interfaces grounded in consistent definitions.
Product engineering
Production web applications that connect data, backend services, and focused user experiences into one coherent system.
Durable systems over fashionable complexity.
I am a data engineer with a background in business intelligence and an MSc in Economics, building end-to-end products that connect data, software, and decision-making.
My background in economics trained me to analyse complex systems, understand how their parts interact, and make deliberate trade-offs under real constraints. I apply that same thinking to engineering by building dependable pipelines, consistent data models, understandable interfaces, and infrastructure that remains simple to operate.
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