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Data platform and warehouse review

Somewhere around fifty people, the numbers stop matching: sales, finance and product each have their own spreadsheet and their own truth. A data-platform review maps where data comes from, how it is transformed and where it breaks, then proposes a warehouse and pipeline design the team can actually run.

02 · experts matched 26 cases across their profiles Contracted through Invental Request a review ↗
Lead match
Code review · Arch. review · Arch. design · Due diligence · Vibe-code rescue

Founding data engineer: DWH, ML and LLM systems from zero

Best for: solo founder · seed startup · scale-up (especially the ~50-person "our numbers don't match" stage).

senior data / ML engineer with more than a decade in data engineering · European time zones View profile →
Lead match · 01 senior data / ML engineer with more than a decade in data engineering
— Also matched for this
founder-CTO / founding product engineer

0→1 founder-CTO for data-heavy SaaS and agent-ready tooling

Best for: solo founders and seed startups building a data or AI product · SaaS teams adding an API or MCP server · developer-tool companies whose CLI or API will be called by AI agents · founders who want product and engineering advice in one person.

Code review, Arch. review +4View profile →

From the network

Selected cases from these experts’ profiles.

A company data warehouse from scratch

Data was scattered across multiple heterogeneous sources and there was no single place to report from.

One of our experts designed and implemented the company's data warehouse from scratch: ingestion pipelines from every source, BI-ready marts, Tableau reporting, plus hiring and onboarding data analysts.

Outcome

One warehouse and one set of numbers the company could report from.

Track record · tech / product company · growing companyClickHouse, PostgreSQL, MySQL, Airflow, Tableau
Founding data engineer: DWH, ML and LLM systems from zero →

Data audit for a company at the "~50 people, numbers don't match" stage

Reports are built by hand in spreadsheets, departments disagree on the same metric, and one person holds the whole analytics setup in their head.

Inventory the sources and existing reports, check metric definitions and pipeline reliability, and look at bus-factor and access risks.

You get

A risk map, a target data architecture sized to the company, and a phased plan the in-house team can run.

Bookable · Audit / due diligence · Architecture reviewdepends on findings (typically PostgreSQL / ClickHouse, Airflow / dbt, a BI tool)
Founding data engineer: DWH, ML and LLM systems from zero →

LLM-based data-quality checks in production pipelines

Production analytics and ML pipelines pulled from many sources, and bad data was caught late.

One of our experts builds and supports the pipelines and automated data-quality monitoring, including LLM-based checks, and productionises data-science outputs for downstream use.

Outcome

Data-quality issues are flagged automatically before they reach dashboards and models.

Track record · data company (confidential) · n/aPython, ETL / ELT, LLM-based checks, BI
Founding data engineer: DWH, ML and LLM systems from zero →

Data pipelines at millions of records per day

The same SaaS tracked millions of apps: installs, revenue estimates, rankings, SDK usage, store-page changes. Our expert designed pipelines that process millions of records daily, with queues and workers for ingestion, a columnar store for analytics and a search engine for lookups. This powers search, reports and customer-facing analytics.

Track record · B2B data / analytics · small SaaS teamPython, Celery / RabbitMQ, ClickHouse, Elasticsearch, MySQL, Redis
0→1 founder-CTO for data-heavy SaaS and agent-ready tooling →

Questions buyers ask

When does a company need a data warehouse?+
When reporting questions start taking days, when the same metric has different values in different tools, or when analysts query the production database directly. Those are signs that the data needs one modeled place of record.
What does a data audit deliver?+
A map of sources and pipelines, the places where numbers diverge and why, and a target architecture with an ordered migration plan, from quick fixes to the warehouse itself.
At what company size do you need a proper data function?+
A data engineer in our network has seen the same pattern many times: around 50 employees, the numbers stop matching between departments and one overloaded person owns storage, reports and analytics at once. In their view a company can't grow much past 100 people without a real data function, so the cheapest time to lay the foundation is before that.

How it works

  1. Tell us what you need — the repo or system, the question, and the deadline.
  2. We match an expert from the network, with a second reviewer where it helps.
  3. Scoped work, contracted through Invental — review per pull request, a fixed-scope audit or architecture review, or ongoing capacity.

— Invental · software studio · Montevideo, UY

Tell us what needs a look.

Describe the system and the question. We match a lead expert from this page, or a better fit from the network, and confirm scope before anything starts.

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