Vibes are how you ship apps, not defensible numbers

The same Slack thread, every quarter:"Which number is right?"

A focused audit of your data stack    The sources behind every number    The definitions behind every KPI    The reports behind every decision A prioritized plan for trustworthy metrics.

The problem

If the answer depends on who you ask, you have a data problem

Most startups don't need a platform rebuild.
They need to know what's broken, what matters, and what to fix first.

Conflicting KPIs

Finance, product, and growth all report different numbers for revenue, churn, and conversion — and each can defend theirs.

Fragile data pipelines

Your models, spreadsheets, and integrations work — until they don't. How they work lives in one person's head.

No clear data roadmap

You know the stack needs work — but not whether modeling, testing, definitions, ownership, or tooling comes first.

What you get

A focused data-stack audit, not an open-ended consulting engagement

A short, fixed-scope review of your analytics setup that surfaces the highest-leverage fixes for reliable, useful reporting.

Data Stack Audit & KPI Alignment

Typical timeline
3–5 business days
Format
Remote, fixed scope
Best for
Seed to Series B, messy setup

Data stack assessment

Review of your warehouse, transformation layer, reporting tools, core sources, and workflow.

KPI definition review

Identify where metrics like revenue, retention, churn, activation, CAC, or active users are inconsistent.

Pipeline & modeling risk

Flag brittle models, undocumented logic, missing tests, unclear ownership, and single points of failure.

Prioritized roadmap

A practical 30/60/90-day plan ranked by business impact, effort, and risk.

Executive-ready audit doc

A concise report your team can use to align engineering, finance, product, and leadership.

Optional readout session

A recorded walkthrough of findings and recommended next steps.

Scope of the review

What I look at

01

Source data & ingestion

  • SaaS tools, product databases, payment systems, CRM, ad platforms
  • Data freshness and reliability
  • Duplicate or unclear sources of truth
02

Warehouse design

  • Snowflake, BigQuery, Postgres, Redshift, or similar
  • Schema organization and access patterns
  • Cost, performance, and maintainability risks
03

dbt / transformation layer

  • Model structure and naming
  • Testing and documentation
  • Dependencies, lineage, and business logic
  • Opportunities for cleaner marts and metric layers
04

Metrics & governance

  • Definitions for revenue, customer, product, and growth metrics
  • Finance vs. product reporting alignment
  • Ownership and change-management process
05

BI & reporting

  • Dashboard sprawl
  • Broken or misleading reports
  • Executive, finance, product, and operational reporting needs
06

Team workflow

  • Who owns data quality
  • Where analysts and engineers are blocked
  • Manual spreadsheet workarounds
  • Prioritization and roadmap gaps
The process

From messy metrics to a clear plan in four steps

01 — Fit call

Fit call

A 20-minute conversation to understand your team, stack, reporting pain points, and goals. If I'm not the right fit, I'll say so quickly.

02 — Discovery

Lightweight discovery

You provide limited read-only access — or simply walk me through the relevant tools, models, and dashboards.

03 — Analysis

Audit & analysis

I trace data from source systems to reporting, focusing on what most affects business decisions.

04 — Readout

Roadmap & readout

You get a concise audit document, prioritized recommendations, and a working session on next steps.

Example deliverable

What you'll walk away with

A concise executive report. Here's the shape of it.

Example: Data Stack Audit Summary Confidential · Illustrative

Executive summary

The highest-priority issue is inconsistent revenue logic across Finance and Product reporting. The team has three separate definitions of "active customer," creating material reporting risk.

Top priorities

  1. Consolidate revenue and customer metric definitions.
  2. Add dbt tests to high-impact finance and product models.
  3. Establish a trusted reporting layer for executive KPIs.
  4. Document source-of-truth ownership for core metrics.

Impact / effort matrix

Low effortHigher effort
Impact →
Standardize KPI definitions
Build governed reporting marts
Remove duplicate dashboards
Rework source ingestion reliability
Illustrative example — deliverables are tailored to each team.
Who it's for

This is a good fit if…

Good fit

  • You're a startup with an existing data stack but limited data-team capacity.
  • Your company has recurring debates about metric definitions.
  • Finance, product, and growth reporting don't fully agree.
  • You have dbt, a warehouse, dashboards, or a growing collection of spreadsheets.
  • You need an experienced outside perspective before hiring or rebuilding.
  • You want a practical roadmap, not a vendor sales pitch.

Not ideal fit

  • You need a full data-platform implementation completed immediately.
  • You don't yet have meaningful source data or reporting needs.
  • You're looking for a general-purpose agency.
  • You need 24/7 production support or embedded full-time staffing.
FAQ

Frequently asked questions

What does the audit cost?

Introductory fixed-price audits start at $750 for qualifying early-stage teams. The exact scope is confirmed after a free fit call.

How long does it take?

Most audits take three to five business days once access and context are available.

Do you need access to all of our production data?

No. The review can begin with architecture walkthroughs, dbt project access, dashboard access, metadata, sample queries, and limited read-only permissions. We'll agree on an access approach that is appropriate for your security requirements.

What tools do you work with?

The audit is most useful for teams using a modern analytics stack: dbt, Snowflake, BigQuery, Postgres, Redshift, Looker, Metabase, Mode, Hex, Fivetran, Airbyte, Segment, and related tools.

Can you help implement the recommendations afterward?

Yes. If it makes sense, implementation can be scoped separately. The audit itself is designed to be valuable even if your internal team executes the roadmap.

Are you available for fractional work?

Potentially. The audit is a good low-risk starting point for teams considering ongoing analytics engineering or data strategy support.

Get started

Get clarity on your data stack before it costs you a decision

Book a short fit call, or send a note about your stack and what's not working. I'll reply personally.

Prefer email? kylek@knoche.dev
Request an audit Response within 2 business days
No obligation. Scope confirmed on a fit call.

Thanks — I received your note and will get back to you within two business days.