Hack'celeration Agency · Data 2026The Data agencythat pipes your sources, builds the warehouse, models with dbt, ships dashboards, keeps numbers honestdecisions on numbers you trust.
The Data agency that builds your modern data stack end to end: ingestion pipelines into a warehouse, dbt models that turn raw rows into tested datasets, and dashboards your team actually opens. Most reporting fails the same way, two numbers for one metric and nobody sure which to believe, so we wire data quality and a semantic layer first, then the dashboards on top.
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GeminiA Data agency builds trust into the numbers, not just charts.
Anyone can plug in a dashboard tool. Piping your sources, modeling raw data with dbt, and keeping a metric meaning one thing everywhere is a different job. Here are the four things we own.
- Ingestion & pipelines
Your sources piped in, clean and on schedule
Data scattered across a CRM, your ad platforms, Stripe and a dozen spreadsheets isn't a stack, it's a mess you reconcile by hand every month. We build the ingestion pipelines (ELT with Fivetran or Airbyte, plus custom connectors for the sources off the shelf) that land your raw data in one warehouse on a schedule. No more exporting CSVs, no more two numbers for the same metric.
See a typical pipeline - Warehouse & modeling
A warehouse and dbt models that turn raw rows into answers
Raw tables don't answer business questions. We set up your warehouse (BigQuery, Snowflake or Postgres, whichever fits your size and budget) and build the dbt models on top: staging, cleaning, tested transformations, and a semantic layer so a metric like revenue or active users means one thing everywhere. Documented, version-controlled, and tested, so the numbers hold up when someone asks where they came from.
See the method - Dashboards & BI
Dashboards your team actually opens on a Monday
A dashboard nobody trusts gets ignored, and people go back to gut feel. We build the BI layer in Looker, Metabase or your existing tool, on top of modeled data so every chart traces back to a tested definition. Self-serve where it makes sense, with the key metrics defined once in the semantic layer. The goal is decisions made on numbers, not on the loudest opinion in the room.
See the integrations - Quality & governance
Data quality and governance, so the numbers stay trustworthy
Trust dies the first time a dashboard is wrong and nobody catches it. We wire data quality tests, freshness checks and alerts so a broken pipeline pings you before it pings your CEO, plus GDPR-aware handling for personal data and reverse-ETL to push clean metrics back into the tools your team works in. We're an automation and AI agency first, so this plugs into how you already operate.
See AI enablement
We build a data stack like infrastructure, not a reporting demo.
Most data projects die the same way: a dashboard tool bought, raw data dumped in, and three charts that disagree until everyone goes back to gut feel. So we treat it like infrastructure: clean ingestion, a right-sized warehouse, dbt models with tests and a semantic layer, then dashboards on top of numbers that hold up.
- Audit · map your sources, your real questions, and where the numbers break today
- Ingest & warehouse · ELT pipelines into a warehouse sized to you, safe by default
- Model · dbt transformations, tests and a semantic layer so a metric means one thing
- Activate · dashboards, alerts and reverse-ETL, handed over with the docs to own it
We run our own data on this stack.
We don't sell a partner tier. We run our own decisions on the same modern data stack: ELT into a warehouse, dbt models with tests, a semantic layer, and dashboards we actually open. That's exactly what's missing when a data project ends at handing over a dashboard nobody can explain.
- We build the modern data stack we'd run ourselves: ELT, a right-sized warehouse, dbt models with tests, not a tangle of brittle scripts.
- Trust by default: every metric is tested and traces to one definition, so a dashboard you can't explain never makes it to a meeting.
- You leave autonomous: the models live in your repo and your warehouse, documented, so your team owns the stack without us.
- Honest about fit: if your data is too messy upstream or your team is too small to need a warehouse, we'll tell you before you spend on one.
A warehouse at the core, your modern data stack around it.
We configure the parts that turn scattered sources into trustworthy reporting, then connect them to how your team already works. Here's what a real data build covers.
- Setup
Ingestion & ELT pipelines
We connect your sources (CRM, ads, billing, product) with ELT tools like Fivetran or Airbyte, plus custom connectors where no off-the-shelf one exists, so raw data lands in your warehouse on a reliable schedule.
- Setup
Warehouse setup
We stand up your warehouse (BigQuery, Snowflake or Postgres) sized to your data and budget, with the schemas, roles and cost controls that keep it fast and predictable instead of a runaway bill.
- Setup
dbt data modeling
We build dbt models that clean, join and transform raw tables into trustworthy datasets: staging, marts, tests, documentation and lineage, all version-controlled so a change is reviewed before it ships.
- Setup
Semantic layer & metrics
We define your core metrics once in a semantic layer, so revenue, active users or margin mean the same thing in every dashboard and report instead of three conflicting numbers.
- Setup
Dashboards & BI
We build the reporting layer in Looker, Metabase or your existing BI tool on top of modeled data, so every chart traces back to a tested definition and the team can self-serve safely.
- Setup
Quality, governance & reverse-ETL
We add data quality tests, freshness checks and alerts, handle personal data in a GDPR-aware way, and use reverse-ETL to push clean metrics back into the tools your team actually works in.
We map your data and your real questions, you leave with a plan.
Before quoting anything, we take 60 minutes to look at your sources, your current reporting and the decisions you actually need the data for. You leave with an honest read on what to build first, whether you even need a warehouse yet, and where the numbers break today. Zero pitch, just an engineer's take on your data.
- An honest read on what your data stack needs first
- The warehouse and pipelines worth setting up
- The metrics to define once and trust everywhere
- A frank take on whether a warehouse is premature
How we run a data stack build.
Five steps, in order. We don't ship a dashboard before the data is modeled and tested, we don't build a warehouse you don't need yet, and your team owns it at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · Data audit
Map your sources and the questions that matter
We sit down with you and look at what you actually need to decide: which metrics drive the business, where the data lives, and why today's numbers don't agree. We check your sources, your current tracking and any existing reporting. Half the value is telling you what to fix first, and whether you even need a warehouse yet, before anyone builds a pipeline.
- Step 2 · Ingest & warehouse
Pipe your sources into a warehouse sized to you
We build the ELT pipelines (Fivetran, Airbyte or custom connectors) that land your raw data in a warehouse, BigQuery, Snowflake or Postgres, sized to your volume and budget. We set roles, schemas and cost controls so it stays fast and the bill stays predictable. Nothing fancy you don't need, just clean raw data in one place on a schedule.
- Step 3 · Model with dbt
Turn raw rows into datasets you can trust
We build the dbt models on top of your raw data: staging, cleaning, joins, tested transformations and a semantic layer so a metric is defined once and means the same thing everywhere. Everything is version-controlled, documented and tested, with lineage, so when someone asks where a number came from there's a clear, reviewable answer instead of a shrug.
- Step 4 · Dashboards & activation
Ship the BI layer and push data back into your tools
We build the dashboards in Looker, Metabase or your existing BI tool on top of modeled data, so every chart traces back to a tested definition. Then we add data quality tests, freshness checks and alerts, and use reverse-ETL to push clean metrics back into the CRM and tools your team already works in. The stack ships with monitoring from day one.
- Step 5 · Hand over & train
Train the team, then get out of the way
We train your team on the stack: how the models work, how to add a metric, how to read the lineage when something looks off. The docs live in your repo so new hires inherit them. If you want to go deeper on the AI and automation side, our training covers that end to end. If you want us on call for what scales next, we talk about that separately.
We're judged on the numbers teams act on.
No partner badge to display, so we lead with what matters: feedback from the teams whose data stack we built, and whether they kept making decisions on those numbers after we left. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The dbt models live in your repo and warehouse, owned by your team
- Every metric defined once, tested, and traceable through lineage
- Quality checks and alerts wired before any dashboard ships
- Trustpilot reviews come from the teams we built the stack for
The questions we get asked on repeat.
What does a data agency actually do?
A data agency builds the data stack that lets you run decisions on trustworthy numbers, instead of reconciling spreadsheets by hand. We pipe your sources into a warehouse with ELT, model the raw data with dbt so every metric has one tested definition, build the dashboards your team actually opens, and wire data quality checks and governance so a broken number gets caught early. The point is decisions made on numbers you can trust, not a pretty chart nobody believes.How much does building a data stack cost?
It depends on scope: a warehouse plus a handful of dbt models and one dashboard is nothing like piping a dozen sources, building a full semantic layer and wiring reverse-ETL into your tools. We don't throw out a flat package. We start with a free 60-minute audit to find what you actually need, then quote a fixed scope. The warehouse and ELT tools (BigQuery, Snowflake, Fivetran) you pay the vendor directly; we set up the cost controls so the bill stays predictable.Which data warehouse should we use, BigQuery, Snowflake or Postgres?
It depends on your volume, your team and your budget, and a big part of the audit is answering this honestly. BigQuery is a strong default when you're already on Google Cloud and want serverless scaling. Snowflake fits when you need fine-grained compute control and multi-cloud. For a smaller team with modest data, a managed Postgres is often plenty and far cheaper. We pick the warehouse that fits where you are, not the one that sounds most impressive.What is dbt and why does our stack need it?
dbt is the transformation layer of the modern data stack: it lets us turn raw tables into clean, tested, documented datasets using version-controlled SQL that runs inside your warehouse (the ELT approach). Without it you get one-off scripts nobody can audit and metrics that disagree. With it, every transformation is reviewed, tested and traceable through lineage, and a metric like revenue is defined once in a semantic layer so it means the same thing in every dashboard.Can you connect this to our existing dashboards and tools?
Yes, that's the activation layer. We build dashboards in Looker, Metabase or whatever BI tool you already use, all on top of modeled data so every chart traces to a tested definition. We also use reverse-ETL to push clean metrics back into the tools your team works in day to day, like your CRM or ad platforms, so the trustworthy numbers show up where decisions actually get made, not just in a reporting tab someone forgets to open.Our data is a mess. Can you still help?
Sometimes, and we'll be straight about it. A data stack is garbage in, garbage out: if there's no disciplined tracking plan and no one owns data quality upstream, the cleanest pipeline still surfaces wrong numbers. Often the first job isn't a warehouse at all, it's a tracking plan and clear ownership so the data arrives consistent. We'll tell you in the audit whether you're ready to build, or whether fixing the source comes first.We're a small team. Do we even need a data warehouse?
Maybe not yet, and we won't sell you one you don't need. If your whole business runs on a couple of tools and a spreadsheet still answers your questions, a full warehouse and dbt setup is overkill and a cost you'll resent. Sometimes the right move is a clean tracking plan and a simple dashboard on top of your existing tools. We'll tell you honestly when a warehouse is premature and what to do instead until you grow into one.How do you keep the data accurate and GDPR-compliant?
Accuracy comes from testing: dbt tests on every model, freshness checks on pipelines, and alerts so a broken source pings the team before it pings a dashboard. Trust is the whole product, so a number that can't be traced doesn't ship. On GDPR, we handle personal data with care: scoped access, minimization, and clear handling of any personally identifiable fields in the warehouse, so your reporting is accurate without turning into a compliance problem down the line.
Stop reconciling spreadsheets. Build a stack you trust.
A 60-minute audit, your sources and real questions mapped, a build plan with data quality baked in. If your team can run it in-house after setup, we'll hand you the playbook. If we're the right fit, we handle it.