Trust & Will

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Director of Data Engineering

Little Big Things

I Consult and advise on data practices and software engineering. I like having a good time making quality products. I love streamlining data workflows and helping companies find real value in their data. Whether it be by reducing data warehouse costs or helping develop models for supply chain forecasting, improving customer segmentation or helping answer which data features a company should invest in.

Data > Architect > Engineer > Analyst 

Vanguard Charitable

Lead Data Architect

BrainStation

I teach at Brainstation on a part-time basis. Everything about  the world of Data & Analytics and Python.

Associate Instructor

adplist.org

I coach everything related to data engineering to identify their goals, craft their skills, present their work, and grow their careers.

I mentored dozens of data engineers and analysts across the world 🌎 but

Data Engineer Mentor

Thrive Global

Leading multiple teams in analytics, data engineering, data science, and machine learning.
Building the next data platform for Thrive.

Focused on defining & executing Thrive Globals Data Engineering Strategy.
Design & Architect Data Platform on Azure/GCP (Argo, BigQuery, DBT,  Looker, Python all the things etc..).
I also advocate best practices for Data Engineering across the organization.

Lead Data Engineer | Manager

Plato 

Data Leadership Mentor

Bonobos

Senior Data Engineer

Languages/frameworks Python,Spark,Terraform, EMR,Kafka,
Databases: PostgreSQL,Snowflake,Redshift
Cloud Platforms: AWS,Heroku
Systems: NetSuite, Salesforce
Javascript: NetSuite Script
* Functional Analyst:*
- Worked on many projects across different areas across the business focusing on supply chain and business operations.
*Other Experience:*
- Led multiple company-wide Hackathons, organizing them with a team and creating documentation and playbooks for future events.

Senior Software Engineer

Kit and Ace

Applications Developer

- Conduct cross-functional financial reporting and gather requirements for reports, dashboards, and KPI analysis.
- Take responsibility for promptly resolving application issues and regularly reviewing internal and external support KPIs and SLAs.
- Manage the application Change Process using Git and NetSuite Suitebundler.
- Gather business requirements and analyze solutions for delivery.
- Create sketches, models, and early user guides or prototypes to showcase new system functionality or enhancements.
- Collaborate with the business and external/internal technical resources to develop and implement solutions.
- Perform system configuration.
- Develop scripts to customize system functionality using JavaScript, SQL, etc.
- Implement and define best practices for customization, scripts, and business workflows.
- Contribute to system enhancements, enable new features, and support business execution.
- Maintain bundles and packages as part of system maintenance.

ERP Business Systems Analyst

PAI Health

- Responsible for ensuring success in financial reporting, requirements gathering, report creation, dashboards, and KPI analysis.
- Implemented improved procedures for NetSuite deployment, resulting in reduced overhead costs.
- Collaborated with finance, sales, and operations to develop distinct KPIs to measure success.
- Created daily reports for inventory management, finance, and marketing, while managing a heavy workload.
- Actively pursued professional development and provided expert advice.
- Managed changes in NetSuite Scope to accommodate new business needs, including global expansion and web store expansion.
- Streamlined NetSuite connectors for warehouses and shipping, resulting in notable success cases.
- Gathered business requirements and developing innovative solutions for various processes.
- Improved communication between factories and sales teams, creating a common language for production, marketing, and sales.
- Analyzed business problems and translated them into system requirements for cost-effective solutions.

Inventory & Forecast Analyst (manager)

Most people see data as a technical problem, but real success comes from aligning data & engineering with business, people, and processes. I'm a self-taught engineer who became a director of data engineering. I provide clear insights on creating solid data foundations. I focus on practical solutions, avoiding getting caught up in tools or jargon.

Ever read books and watched talks but still felt stuck on applying the ideas?

That's me with strategy frameworks. The theory seems great, but using it with real data and teams can get unclear.

So, I’m reaching out to my network.

I need someone to run a hands-on workshop for a small group. We want more than just slides or theory—we want to dive in. Show us how to make these frameworks work with real-world examples with so much AI, Business work going on I think this kind of thinking is becoming ever more critical.

Message me if you're interested, or tag someone you think fits.

Let’s bring these ideas to life - I'm eager to really get into this and apply more.

I’ve really enjoyed the book Architecture For Flow by Susanne Kaiser , Ben Mosior work on Learn Wardley Mapping and conversations and writings by Ergest Xheblati on Decision intelligence. 
I know ✨ Shane Gibson this stuff lights you up and Nick Zervoudis you are applying ROI to data. 

I just think there is holistic pattern matching that I have in my head that needs to be out there I feel so close it's driving me crazy.

However, I wish I could find someone to help me apply this knowledge. I feel close but still miss the mark on real application.

Tim Frazer 🚀

I empower businesses to turn data into a growth engine the right way.

Recently, I watched a candidate's cursor move while his hands were in the air as they "solved" a fairly straightforward python problem. 
"I'm not using AI," he insisted. We both knew he was lying. This was one of the most bizarre interview sessions I've had and frankly, and I'm seeing more of it.

It wasn't a deepfake or some wild story. I even told candidates during the interview process they could use ChatGPT to look things up. Just not to solve the problem directly. I wanted to explore how they thought and worked through problems.

How many folks interviewing are seeing this - are we going back to in person interviews?

I don't always talk about data, for example I've been working on a system with my wife and our four kids ( 4 under 4 years old), two jobs, small business and a busy house -> chaos, unless you run it like a business.

Every Sunday, we do a quick family meeting with an agenda in Notion. Urgent tasks first, every job has an owner, every plan has a date. If it’s not scheduled, it won’t happen.

Simple rules:
- Set dates
- Assign tasks
- Stick to the agenda

It’s not fancy ( I'll talk more about AI and other tricks we use another time), but it keeps us sane. 
Try it it might save your week - take a gander at my post below and let me know what you do to keep the chaos at bay.

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Ciro Greco Jacopo Tagliabue gave a talk about their work on an agentic data lakehouse work at bauplan  and brought lightness, comedy, and passion to a somewhat esoteric subject dear to my heart data engineering and surviving the pain cave of broken pipelines.
Hosted by  DataPhilly  at The Wharton School

I use Claude, Chatgpt, Gemini, others pretty much every day. Yet, I've been through enough startup cycles to spot the pattern forming right now. This won't pop like a bubble. It'll burn through the industry and leave a different landscape behind. Fast. Messy. I've never seen a system overcome our senses so fast, not even TikTok. 🔥

My thinking is:

1/ The founders and engineers who survive this don't disappear. They share something you can't teach in a classroom. Yes, vibe coding may fade, but it still has its value. 

2/ Someone who built the wrong product with a good team in 2025 will make the right product with a smarter team in 2026-2027. 

3/ That experience doesn't evaporate. It stacks, again and again. 

Anyone developing software in their 30s has seen this play out before. The people who lived through the last cycle knew things that the newcomers didn't.

They moved faster. Made fewer mistakes. Built better. When you can, take a look at Dinner Insights by Dion Lim. His point of view is worth considering as we see this unfold.

Or am I off base, and it's just one giant bonfire and we are all just roasting tokens? 🔥

For the longest time, I thought the hardest part of data work was the technology.

But the actual obstacle is getting people to care about the data in the first place.

You can build a perfect pipeline, but if nobody trusts the numbers or knows what to ask, it's useless.

Teams often miss valuable dashboards, insights, or reports. 

They may not know what action to take next. Sometimes, they get the information too late to act. 

Other times, they do not request it; we, the data team, include it as needed.

So now, before I code, I ask the business: what's your biggest obstacle?

What would make your job easier, your actions or outcomes more specific?

If the answer is unclear, I dig deeper.

If you don't start with a real problem, you're putting on a show.

Has anyone else had their work ignored because of this?

Daniel Beach pushing out another great piece and also confirming my deeply held vibe around Data Mesh. I still think domain modeling and ownership is important but the rest is a hard one for me to see a clear value....

Open article: Data Mesh Theology. Dead or Alive? by open.substack.com

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