Data Infrastructure Engineer
Most B2B SaaS companies are overpaying for their data stack—usually by 40-60%.
I spent the last 18 months at Cybrary finding and fixing these inefficiencies: reducing infrastructure costs, improving data quality, and building pipelines that actually work. Now I help other companies do the same.
Currently accepting: Fractional engineering engagements and project-based work for B2B SaaS companies ($5M-$50M ARR).
I write detailed technical breakdowns of data infrastructure projects. Here are some recent deep-dives:
Python • Airflow • Cloud Run • HubSpot
Building custom Airflow pipelines vs. using native integrations. Full technical breakdown with architecture diagrams, ROI analysis, and code.
96% latency reduction 90% cost reduction
More technical write-ups coming soon as I document the BI-in-a-Box build, CRM migration lessons, and other projects.
Hands-on execution of specific data infrastructure projects:
Projects include documentation, knowledge transfer, and support during transition.
For companies who need consistent data engineering support without full-time headcount. This works well if:
Typical engagement: Ongoing availability with weekly check-ins and ad-hoc project work as needed.
Technologies I work with:
Python SQL Snowflake HubSpot Salesforce Google Cloud Platform Airflow dbt Docker Terraform
Areas of expertise:
You need someone asking: "Do we actually need this?"
Pick the right tool for the team you have today. Most startups sign commitments with big names too early. Why? Because it feels like progress. However, new complicated rarely just fix all your problems, they usually create new ones.
Labor costs are usually what led to a buy decision due to the ongoing maintanence time needed to keep custom solutions running. Is it time to reasses?
My current thesis is that AI tooling does not eliminate tech debt but makes it easier for smaller teams to manage: faster MVPs, better testing, and automatic PRs for bugs
We found 52k invalid emails in our CRM (9.4% of the list). That's not just a deliverability problem. It means marketing budget wasted on fake contacts, storage overage fees, and sales time spent on bad leads.
Email validation costs ~$0.001 per check. If it prevents even one wasted marketing campaign, it pays for itself 100x over.
Active research project of mine. For companies under $10M ARR or internal reporting use cases, paying $2k/mo for Snowflake might be overkill. DuckDB + MotherDuck gives you enterprise-grade SQL for pennies. You can run the same queries, use the same dbt models, and pay 1/100th the cost.
I'm building a "BI-in-a-Box" reference architecture (dlt + dbt + DuckDB + Evidence) that gives you Snowflake-class analytics for <$5k/year total. Coming soon.
I work best with B2B SaaS companies ($5M-$50M ARR) who are:
Industries I've worked in: Cybersecurity, EdTech, B2B SaaS. But the patterns are similar across verticals — if you're paying for data tools, I can probably help optimize them.
If you're spending significant budget on your data stack and wondering if there's a better way, let's have a conversation.
Response time: Usually within 24 hours. I'm based in Richmond, VA and work remotely with clients across the US.