A warehouse and pipelines that stay up
They design or take over the warehouse and the pipelines feeding it, so data from your CRM, finance system, website and ad platforms lands on schedule and fails loudly when it does not.
Fractional Teammates · AI & Automation
One set of numbers everyone trusts, without a full-time hire
The warehouse, the pipelines, the definitions everybody argues about, and a board pack built from the same modelled data as the dashboards. A senior OlDevs specialist owns that layer on agreed days each week.
A senior specialist, not a junior placement You own the work and the accounts Reply within one business day
Forecast engine
Demand forecast - next 8 weeks
ActualModelledConfidence88%
94.2%
Accuracy
+18%
Vs baseline
8w
Horizon
Retrained nightly on 14 months of order behaviour.
What a data & analytics specialist is
OlDevs fractional data and analytics specialists own the layer your decisions rest on, for agreed days each week.The work is concrete: build or take over the warehouse and the pipelines feeding it, write the metric definitions finance, operations and marketing all sign off, model the data so a question has one answer, and produce the dashboards and board or funder reporting that survive scrutiny. Data quality tests, access rules and documentation come with it. The specialist is a senior member of the Vancouver studio, working inside your tools and your reporting cadence, with the rest of the studio behind them when a job needs a second discipline.
Key facts
- 01Engagement shape
- Agreed days each week, ongoing
- 02Where the work happens
- Your warehouse, BI tools and repositories
- 03Typical stack
- SQL, dbt, Airflow, Looker Studio, Power BI
- 04Reporting
- Weekly demo, quarterly review
- 05Typical commitment
- 1–2 days a week
- 06Judged on
- Definitions agreed · Pipeline freshness · Report turnaround
AI & Automation
What the specialist owns
What this teammate takes off your plate
Definitions everyone signs off on
They write down what an active member, a qualified lead or a completed case actually means, get finance, operations and marketing to agree, and encode the result once so every report matches.
Dashboards people open on Monday
They build the small number of dashboards your team will actually use, tuned to the decisions each group makes, and retire the ones nobody reads. Built to WCAG 2.2 AA.
Board and funder reporting that holds up
They produce the quarterly board pack, funder return or regulator submission from the same modelled data as the dashboards, with the method documented so a question about a number has an answer.
Data quality, access and privacy
They set tests on the pipelines, alerting when a load breaks or a number moves impossibly, and keep access, retention and personal-data handling in line with your obligations in Canada and the United States.
Answers to the awkward one-off question
When the executive team asks why renewals dipped or which programme costs most to run, they do the analysis properly, show the working, and say plainly how confident the answer is.
Benefits
What changes when everyone trusts the numbers
01
Meetings argue about decisions, not numbers
Once a figure has one agreed definition and one source, meetings stop opening with a debate about whose export is right. The disagreement moves to what to do about the number, which is the argument worth having.
02
Month-end stops taking over the team
Reporting runs from the same pipeline every cycle, so nobody rebuilds it by hand under deadline. When a director asks where a number came from, the answer arrives in the meeting rather than the following week.
03
Teams look up figures themselves
People who can answer their own questions stop queueing behind one analyst for every figure. The requests that still arrive are the genuinely hard ones, which is the work you wanted an analyst doing in the first place.
04
Broken data surfaces before anyone acts
A failed sync or a renamed column gets caught by a check that runs with the pipeline, not by someone spotting an odd figure in a board pack. Mistrust of the numbers usually starts there, and it is slow to undo.
05
Grain and tooling get decided once
What a row represents, which warehouse, what to model and what to leave alone: choices you live with for years. They get made by someone who has made them in several organisations, at a depth one company's data rarely keeps busy.
06
Handover is a conversation, not a rebuild
Pipelines live in your repository, credentials in your accounts, and every definition is written where your team can read it. Nothing depends on one person's memory, so whoever takes the reporting on next starts from what exists.
How it works
How the engagement runs
Scoping call
We talk through the decisions you need better numbers for, what already exists, and how many days a week the work realistically needs. Then we quote.
Audit of what you have
In the first two weeks the specialist maps your sources, current reports and the places two numbers disagree, and gives you a written picture of the state of your data.
Definitions before dashboards
Nothing gets built on numbers nobody agrees with. Definitions are drafted, argued over with the people who own each function, then signed off and written down.
Embedded in your week
They join your standups, ticket board and chat channels on the agreed days, and requests reach them the same way requests reach anyone else on your team.
Weekly demo
Every week you see what shipped: a pipeline, a definition, a dashboard, a piece of analysis. Nothing stays described as in progress for a month with nothing to look at.
Who it's for
Who this suits, and who it does not
Reporting problems are political as much as technical, so the fix needs someone senior enough to settle a definition and still be there next quarter to defend it.
Organisations reporting to a board or a funder
You have to show numbers to people who will question them. Someone senior needs to own the definitions, the method and the reconciliation, but the load is not full-time.
Teams where every report disagrees
Finance, operations and marketing each have their own version of the same number. The fix is agreed definitions and one modelled source, which is a job for a senior person.
Companies whose analyst has no one above them
You already have someone capable in the seat, but nobody to set the architecture, review the modelling and take the board conversation. The fractional teammate does that part.
When you should hire instead
If the data work is genuinely full-time, or it must sit inside a controlled environment only employees may enter, hire. Fractional suits a real need that does not yet fill a week.
12+
Years of studio experience since 2014
1–2
Typical commitment — 1–2 days a week
EN/FR
Languages this engagement can be delivered in
Other fractional roles
Often paired with
01
Machine Learning Engineer
A senior ML engineer joins a set number of days in your week, inside your repositories and your cloud tenancy.
Explore02Conversion & Analytics Specialist
One senior specialist owns your GA4 and server-side tracking, the event design underneath it, and the experiment programme that tests pages, forms and funnels.
Explore03AI Automation Specialist
Your teammate spends the first weeks counting how often each task is repeated, then automates what is worth automating: triage, routing, document handling, agent workflows.
ExploreFAQ
Questions from the board, finance and IT
You do, and the specialist runs the argument to a conclusion. They put the competing definitions side by side, show what each one does to the reported number, and get the function owners to pick one in a meeting with a decision recorded. Then it is encoded once, in the model rather than in six spreadsheets, and every report inherits it. Most reporting disputes turn out to be definition disputes.
An agency delivers a reporting project from outside and hands over a deck; a BI vendor sells you a tool and a template, and the modelling problem stays where it was. Neither sits in your standup when the board asks why a number moved. A fractional teammate works inside your warehouse and your rituals on agreed days. If the load genuinely fills a week, hire; we will say so.
They stay with you, because they were never anywhere else. The warehouse sits in your cloud project, the pipeline code in your repository, the dashboards on your own licences, and the documentation beside them. Notice periods are set in the contract. In the final weeks the specialist writes the runbook, records a walkthrough of the model layer, and hands over to whoever picks the work up.
When the queue genuinely fills a week, every week — a shared seat then becomes the bottleneck rather than the fix. When the data may only be touched inside an environment that employees alone may enter. When someone must be at a desk all day for live operational calls. And when what you want is one fixed piece of work with an end date; that is a project, and we would quote it as one.
Nothing moves. The specialist works in whatever you already run, and access is granted by you, scoped to what the work needs, and revoked by you. Personal-data handling, retention and any cross-border question are agreed in writing before anyone connects, against your obligations in Canada and the United States. Where a tool is genuinely holding you back we will say so, with reasons, rather than replacing it by default.
No, that is the usual starting point, and it is what the opening audit is for. The first weeks map sources, find where two systems disagree, and put a written state of your data in front of you before anything is built. What it cannot fix is a number nobody has ever recorded. Where the raw material does not exist, we say so and the fix becomes a collection problem.
Let’s connect
Let’s talk about the data & analytics specialist gap.
Tell us what is not getting done and roughly how much of a week it needs. We’ll reply within one business day with who would cover it and a tailored quote — no obligation.