AI, Data & Machine Learning

Data Engineering

Most early-stage 'data platforms' are a Postgres replica and a Notion doc. We 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.

A violet polished manifold where wide rough-cast inlets merge through a junction block into a few clean precise outlets.

What the work involves

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 get

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 we reach for

Microsoft Fabric
Azure
PySpark
Python
Delta Lake
OneLake
Purview
dbt

Where we’ve done this

Global electronics & healthcare manufacturer

Supply chain / operations planning

Explainable inventory-allocation engine — a transparent linear program that splits factory output across distribution centers at lowest landed cost.

Data Science & Decision Systems
Full-Stack Engineering
Data Engineering
Architecture & Platform Design

Enterprise

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