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Data Engineering
Most early-stage 'data platforms' are a Postgres replica and a Notion doc. I build the real thing — Fabric on Azure, PySpark for transformation, a medallion lakehouse, and a semantic layer your product and AI features can both query against.
What this looks like
Ingestion
- Batch and streaming ingestion into Fabric / OneLake
- CDC from operational stores (Postgres, MongoDB, SAP)
- Third-party SaaS and webhook integrations
Transformation
- PySpark notebooks and Spark jobs for bronze → silver → gold
- Python + dbt-style modelling on the gold layer
- Data quality contracts and idempotent pipelines
Semantic layer & governance
- Semantic models for BI and AI consumption
- Lineage, catalog, and access control (Purview)
- DataOps: CI for notebooks, versioning, environment promotion
What you walk away with
- Production lakehouse on Fabric (bronze / silver / gold)
- PySpark pipelines under CI with data quality checks
- Semantic layer wired into BI and AI/LLM consumers
Tools & tech
Microsoft Fabric
Azure
PySpark
Python
Delta Lake
OneLake
Purview
dbt
More services
View all →Fractional CTO Leadership
Embedded as your part-time CTO. Own technical direction, unblock the team, and report into the board.
Fundraising & Deal Support
Pre-seed to seed fundraising from the founder's side of the table. Legal, financial, and technical workstreams run in parallel — so you close faster and cleaner.
Product Management
Ship the right features at the right time. Roadmaps tied to real user outcomes, not founder instinct.
Ready to scale your engineering?
Book a 30-minute discovery call. If we're not a fit, I'll tell you on the call — and point you toward someone who is.