I turn complexity into actionable intelligence.
I'm Adam Young, a Principal Data Architect. I work at the seam between raw, messy data and the decisions it's supposed to drive — building the connective tissue that turns analysis into infrastructure teams can rely on. This page is the short version of how I think, how I work, and what that looks like in practice.
Most teams don't have a data problem. They have a translation problem.
The raw material is almost always there — events, logs, transactions, a decade of accumulated systems. What's missing is the path from that sprawl to a decision someone can defend on Monday morning. I think of it as a pipeline, and value leaks at every hand-off:
I find those gaps and build the connective tissue that closes them — the reliable pipeline, the trusted metric, the model that ships and stays monitored.
Done well, it doesn't look heroic. It looks like a team that stops arguing about whose number is right and starts arguing about what to do next — which is exactly the argument worth having.
Six principles I bring to every engagement
Outcomes over outputs
A dashboard nobody acts on is decoration. I start from the decision that needs to get better and work backwards to the data — not the other way around.
Trust is the real deliverable
A number people believe beats a clever model they quietly ignore. Most of the work is making metrics legible, reconciled, and owned — so they survive scrutiny in the room where it counts.
Operationalize, don't just analyze
Value shows up when the work runs reliably without me — scheduled, monitored, and resilient to the 3am page. I build analysis into infrastructure, not one-off artifacts.
Make the complex legible
I translate fluently between the warehouse and the boardroom. The hard part is rarely the math; it's turning a tangle of systems into a story a team can act on with confidence.
Build for the handoff
The best engagement leaves a team stronger than I found it — with patterns, documentation, and habits they own. I'm a conduit, not a dependency.
Pragmatism ships
The boring solution that ships this quarter beats the elegant one that never lands. I optimize for momentum and maintainability over novelty for its own sake.
I write to think in public
AI agents won't replace your analysts — they'll kill the ad-hoc queue
The real win from agentic analytics isn't replacing people. It's retiring the endless backlog of "can you pull this?" — and freeing analysts for the work that compounds.
The data model is the product
Dashboards, metrics, and AI agents are all just views of the model underneath them. Modeling is the highest-leverage work in analytics — and the first thing teams skip.
Designing a star schema, step by step
The case for dimensional modeling is easy to make and hard to execute. Here's the actual four-step method — process, grain, dimensions, facts — that turns the idea of a star schema into one that holds up.
The semantic layer is the missing piece for trustworthy AI analysis
Point an LLM at raw tables and it will confidently average the wrong column. A semantic layer is what turns a fluent guesser into a grounded analyst.
Why the star schema still wins
Columnar warehouses were supposed to kill dimensional modeling. Thirty years on, the Kimball star is still the most legible, durable shape for analytics — and the reasons are worth understanding.
The three kinds of fact table
Transaction, periodic snapshot, accumulating snapshot. Most modeling mistakes come from forcing one kind of question onto the wrong kind of fact table — here's how to tell them apart.
Giving an agent a map: grounding LLMs in your data model
An agent is only as good as what you let it see. A practical look at the context, tools, and retrieval that turn a data model into something an LLM can navigate.
Declare the grain before you write a line of SQL
The single most important modeling decision is what one row of your fact table means. Get it fuzzy and every metric built on top is subtly, expensively wrong.
Designing dimensions people actually enjoy querying
Facts get the attention; dimensions decide whether the warehouse is a pleasure or a chore to use. Wide, denormalized, richly attributed dimensions are the difference — and the date dimension is where to start.
From question to query to action: building an agentic analytics pipeline
A reliable agentic analytics flow looks a lot like a data pipeline: discrete, observable stages — plan, retrieve, query, validate, answer, act — not one magic prompt.
Slowly changing dimensions, in plain English
A customer moves from Texas to New York. Do last year's sales move with them? That's the entire question slowly changing dimensions answer — and defaulting to the wrong one silently rewrites your history.
Use surrogate keys, not the source system's IDs
It's tempting to join facts to dimensions on the natural key from the source system. It's also the decision that quietly blocks history tracking, breaks on re-platforming, and slows every query. Here's why a meaningless integer wins.
Guardrails: letting an AI agent touch production data safely
The leap from an agent that answers questions to one that takes actions is where the risk lives. The controls that let you make that leap without losing sleep.
Metrics that survive reorgs
Every reorg arrives with a new VP who has opinions about what "active user" means. A model built on conformed dimensions and stable grain outlives the org chart that questioned it.
Five ways a star schema goes wrong
A star schema can be technically a star and still be a mess. The five failure modes I see most — mixed grain, over-snowflaking, non-conformed dimensions, natural keys, and fact-to-fact joins — and how to avoid each.
Why your dashboards aren't driving decisions
Most BI tools answer questions nobody is asking. The fix isn't a better chart — it's tying every metric to an owner, a decision, and a threshold, then deleting the rest.
The hidden cost of untyped pipelines
An upstream column silently changes type and three dashboards start lying for a week before anyone notices. Schema drift is a tax you pay in 3am pages — and data contracts pay it down.
From notebook to production
The analysis works on your laptop. Now it has to run at 6am, survive a dependency bump, and not page you when it does. The short checklist that gets a one-off to run reliably, without you.
Proof, not promises
Cut warehouse spend 43% in one quarter
A cost and performance audit that found runaway queries, idle clusters, and a quarter's worth of savings hiding in plain sight.
−43%Self-serve analytics for 200 people
A semantic layer and metric framework that took analytics from a ticket queue to a tool the whole company opens daily.
3× adoptionInventory forecasting that cut stockouts
A pragmatic demand model, monitored and retrained on schedule, that kept shelves stocked without ballooning inventory.
−61% stockoutsHave data that should be doing more?
Tell me about the pipeline that breaks, the metric nobody trusts, or the analysis stuck in a notebook. Let's operationalize it.