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Built a reusable Azure Data Factory + Databricks ingestion framework to onboard enterprise Oracle and operational data into a governed lakehouse. The solution used configuration-driven onboarding, incremental loading, watermarks, validation rules and run-level audit history to reduce bespoke pipeline work and improve recovery. I implemented Bronze / Silver / Gold processing, schema and business validation, deduplication, targeted reruns and operational monitoring, helping create a more reliable and scalable foundation for reporting and analytics.
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CSV preparation — an independent Python workflow for splitting tagged records and mapping columns to a supplied schema.
The synthetic example preserves text IDs and multiline fields, stops on unknown tags, and reports output counts and file hashes. Multi-tag routing is explicit, so extra output rows can be explained rather than mistaken for duplicates.
Code and worked example: https://github.com/sjdoane/csv-tag-splitter
Admivo.in — Full-Stack Product
Built the complete Admivo.in ecosystem from scratch for a foreign education consultancy, covering the student-facing platform and internal operations. The system includes country/destination discovery, application journey, counsellor workflows, resource libraries, live admission updates, document management, sessions, and an admin dashboard for managing the entire ecosystem.
Does someone on your team lose a day or two building the sales report every week or month?
That was the starting point at Mokobara, the premium travel brand. Pulling sales across all their stores, checking returns, reconciling the numbers and emailing each manager took 1 to 2 days every cycle, with 8+ hours of that spent just reconciling.
I built them an automation on Make.com a few months ago. It has run on its own ever since, weekly and monthly:
Make pulls every sales record from BigQuery, 100K to 250K per cycle. That is too many for one request, so it reads them page by page and stitches them back together.
It calculates the numbers the team actually uses: net sales per store after returns and discounts, return rates and the change against the last period.
It builds a CSV with the full breakdown and a short email summary you can read in 60 seconds.
It reads a Google Sheet of store representatives and emails every one of them the report for their own store.
The result: the reporting problem is gone. The report went from 1 to 2 days of manual work to fully automatic, and the 8+ hours of reconciliation dropped to zero.
Two things I would do the same way again:
Calculate "net sales" in the automation, not in the warehouse. The business rule for what counts as a net sale is not what the raw data stores.
Send people their slice, not the whole report. One report for everyone gets skimmed by everyone.
The figures in the image are placeholders, the real ones stay with the client.
What report is your team still building by hand? Tell me where the data lives and I will tell you how I would automate it.
