The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Challenges
View all
Related posts
Loved seeing what Vision Engine can create. Designed this final CTA section using Vision Engine, the results turned out beautifully! ✨
Hope you like it :)
Good CRM reporting is only as reliable as the data behind it.
When I started working with Momentum AMP, one of the biggest challenges was inconsistent HubSpot data, which made it difficult for the team to fully trust its reporting.
What initially started as a data analytics engagement developed into a much broader HubSpot development and operations partnership.
I introduced daily data-quality monitoring to identify and resolve issues before they could affect reporting, while also building and improving automation across the CRM.
The work expanded into sales commission workflows, weighted deal assignment, customer success processes, reporting improvements and wider operational automation. I also supported projects including Stripe data cleanup and a Gemini AI integration.
For the SCC team specifically, I automated processes around lead rotation, outreach, post-mortem tracking and email distribution — reducing the amount of manual work required across their day-to-day operations.
The engagement has now covered more than 390 CRM tasks, with data-quality checks running every working day since September 2024.
The result is a HubSpot environment with more reliable data, stronger automation and reporting the team can actually trust when making decisions.
For me, this project shows why CRM optimisation starts with the fundamentals. You can build sophisticated workflows and dashboards, but if the underlying data isn’t reliable, the rest of the system can’t deliver its full value.
From Airflow DAGs to a lightweight dashboard-only mode.
I developed an automated ETL pipeline for London weather data. The pipeline handles Extract (REST API polling), Transform (data cleaning & normalization), and Load (SQLite with timestamps). The dashboard shows Current Temp 23.3°C, PM2.5 4.30 µg/m³, historical trends, and raw data table.
Key decisions: switched from full Airflow setup to a lightweight Streamlit app for faster deployment and lower resource usage, while keeping the code clean and reproducible.
Repo structure: dags / src / dashboard / data / scripts - ready to be used as a template for any weather/IoT data project.
