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I will audit and fix your data quality so your ai gives the right answers
Microsoft Fabric and Power Platform data architect
Data architect specialising in Microsoft Fabric and the Power Platform. I design and build the back end that makes data trustworthy: lakehouse architecture, PySpark pipelines, data quality checks, and...
Über diesen Service
Same question, different data, different answer. If your AI or reports are fed duplicated, inconsistent or missing data, they will give confident wrong answers. I find and fix those problems.
I'm a working data architect who runs data quality frameworks on production platforms in a regulated environment.
What the audit covers:
- Profiling: completeness, duplicates, formats and outliers
- Issue log: every problem, where it is and how big it is
- Rule-based checks that run on every load
- Quarantine, so bad records are held, never lost
- A pass-rate scorecard with alerts
- A fix plan prioritised by business impact
How it works:
- We agree a fixed scope before anything starts
- I audit your data and share the issue log
- I build the checks and fixes
- You get a walkthrough, documentation and full ownership
Works on Databricks, Microsoft Fabric and most SQL platforms. Please message me before ordering so I can confirm the right package.
Tools und Plattformen:
Azure Data Factory
Mein Portfolio
Meine weiteren Dienstleistungen im Bereich Datentechnik
FAQ
Which platforms do you work with?
Databricks and Microsoft Fabric as standard, both using PySpark and Delta Lake. If your data lives elsewhere, message me first and I will confirm whether I can help before you order.
Can you add checks to my existing pipelines?
Yes. The framework is designed to slot into existing pipelines, so validation and quarantine can be added without rebuilding what you already have.
How do I add new rules after delivery?
Rules are stored as configuration rather than hard-coded, so your team can add or adjust checks without touching the pipeline code. The rules catalogue and runbook explain exactly how.
