Data Engineering Blog
Practical writing on data engineering, analytics infrastructure, and building systems that scale. Hands-on guides on dbt, BigQuery, data warehouses, and analytics engineering, plus lessons from real client builds in our data engineering case studies.
3 Signs Your Company's Data Is a Mess (Not Just Annoying)
Messy data doesn't always look like an outage. Usually it looks like AI giving useless answers, nobody knowing where a number lives, and decisions made on vibes because checking the real number was too much of a hassle. Here's how to tell which one you have.
data engineering · data stack · AI-ready data · tribal knowledge · decision making
Read the article →How We Approach Data Engineering (Data Is Water, We're the Plumbers)
Our approach in one line: data is water, we're the plumbers, and there's a big difference between pipes that technically carry water and pipes built to last. Here's how that shows up in what we build, how we test, and how we communicate.
philosophy · testing · communication
Read post →How We Work With a Data Stack That's Already There
Here's the actual process for stepping into a data stack someone else built: what we ask first, why we read the code before we diagram it, and how a risk register keeps us from breaking something nobody mentioned.
existing data stack · legacy data · risk register
Read post →5 Returns on Data Infrastructure That Compound Over Time
Data infrastructure doesn't pay off all at once. The returns come in waves, each building on the last, from basic visibility all the way to AI agents that serve the whole company.
data infrastructure · data warehouse · ROI
Read post →What AI Needs from Your Data (4 Bars to Clear Before You Trust the Output)
AI tools only give reliable answers when the data underneath meets four bars: clean schema, tested and validated, classified and governed, and documented lineage. Most company data clears zero.
AI-ready data · data quality · data governance
Read post →How to Stop Your Company's Data From Living in One Person's Head
When only one or two people know where the numbers live and how to pull them, your company has a tribal knowledge problem. Data engineering turns that into infrastructure everyone can use.
data engineering · single source of truth · startups
Read post →6 Ways to Get Data Engineering Done (And How to Choose)
Full-time hire, freelancer, fractional, agency, managed service, or DIY platform, each model works for a specific stage. Here's an honest comparison so you pick the one that fits.
hiring · fractional · data engineering
Read post →Why Your Sales and Finance Teams Disagree on Revenue
Sales says one number, finance says another, and the CEO asks which one is real. The problem isn't the people. It's that each function defines revenue differently and there's no shared source of truth.
reporting · single source of truth · revenue
Read post →Why Your Monday Reports Never Match
When different people pull the same metric and get different numbers, the problem isn't the people. It's that your data has no single source of truth. Here's why it happens and what to do about it.
reporting · single source of truth · data quality
Read post →Why Your Team Keeps Asking You for the Same Numbers
If you keep fielding the same data requests every week, it's not a people problem. It's a systems problem. Here's what's really going on and how to fix it.
reporting · single source of truth · startups
Read post →How to Consolidate Data From Multiple Tools
To consolidate data from multiple tools, pipe each source into one central data warehouse on a schedule, model it into shared definitions, and point every report at that single source of truth. Here's the modern approach, and the manual traps to avoid.
data integration · single source of truth · startups
Read post →How to Replace Spreadsheets With a Data Warehouse
Replace spreadsheets with a data warehouse when version chaos, manual joins, and slow recalcs start costing you real time and wrong numbers. Here's what a warehouse gives you, when to switch, and the migration path that doesn't disrupt the business.
data warehouse · spreadsheets · startups
Read post →When to Hire a Data Engineer (7 Signals It's Time)
You need a data engineer when your analysts spend more time collecting data than analyzing it, your reports keep breaking, or no two dashboards agree. Here are the concrete signals, and why fractional usually beats a full-time hire first.
hiring · data engineering · startups
Read post →Data Engineering for Nonprofits: Why You Need It but Shouldn't Hire for It
Nonprofits collect a lot of data but most can't answer a basic board question without a week of manual work. The problem is data engineering, and a full-time hire isn't the answer.
nonprofits · data engineering · fractional
Read post →Why AI Gives Wrong Answers on Your Company Data
When an AI tool gives wrong answers about your company data, the cause is almost never the model. It's the data underneath. Here's how to make it AI-ready.
AI-ready data · data quality · data infrastructure
Read post →What Is Fractional Data Engineering? What Is This, Who Needs It, How It Works?
Fractional data engineering is hiring a senior data engineer on a part-time, retainer basis instead of full-time. Here's what it is, who it's for, and how an engagement works.
fractional · data engineering · startups
Read post →What to Ask a Data Engineer Before Hiring Them
Most data engineer interviews are designed by engineers, for engineers. If you're a non-technical founder, these questions will help you understand whether the person in front of you has the experience your situation actually requires.
hiring · data engineering · interview
Read post →What Does a Data Engineer Actually Do All Day?
If you're thinking about hiring a data engineer and you're not technical, here's the plain-English answer: what will this person do, what will I see from them, and how do I know if they're doing good work?
data engineering · hiring · startups
Read post →How to Set Up dbt for the First Time (For Small Teams)
dbt has become the standard for data transformation. If you have a warehouse and you're writing SQL to clean your data, here's how to set it up without overengineering it.
dbt · data transformation · data engineering
Read post →Signs Your Data Stack Needs to Be Rebuilt, Not Just Fixed
There's a version where you fix what's broken and move on. Then there's the other version, where the same things keep breaking in different places. Here's how to tell which one you're in.
data engineering · technical debt · data stack
Read post →Do I Need a Data Warehouse? A Plain-English Guide for Non-Technical Founders
A data warehouse sounds like something large companies have. Depending on where you are, you might need one sooner than you think, or not yet at all. Here's how to tell.
data warehouse · BigQuery · Snowflake
Read post →Data Engineer vs. Data Analyst: Which to Hire First
Data engineer vs data analyst: engineers build the data foundation, analysts turn it into insight. Hire the wrong one and you'll have someone with no infrastructure to analyze, or pipelines nobody uses. Here's which you need first.
hiring · data analyst · data engineering
Read post →How to Leverage AI for Data Analytics (You Need a Data Infrastructure First)
AI analytics tools are impressive. But garbage data at AI speed is still garbage, just faster. Here's why the foundation comes before the AI layer.
AI · analytics · data infrastructure
Read post →How to Set Up Apache Airflow for a Small Data Team
Airflow is powerful and often set up wrong. Here's a practical guide for teams of 1–3 data people who need real pipeline orchestration without it becoming a project in itself.
airflow · orchestration · data engineering
Read post →How to Build a Data Stack from Scratch at a Startup with No Data Engineer
You don't need a data engineer to get started. Modern tools make DIY infrastructure genuinely viable. The issue is what happens when you need to scale.
data stack · startups · dbt
Read post →The Real Cost of Hiring a Senior Data Engineer vs. Going Fractional
Base salary is the wrong number to start with. The true cost of a senior data engineer hire is $200K–$250K/year and that doesn't account for hiring the wrong person.
hiring · fractional · cost
Read post →How to Get Your Team to Actually Use Metabase
You set up Metabase, built dashboards, and sent the link. Three months later, you're the only person who opens it. This is a rollout problem. Here's how to fix it.
metabase · analytics · data adoption
Read post →Ready to fix your data infrastructure?
Book a free 1-hour data strategy call and we'll tell you exactly what we'd build and why.