Warehousing
Snowflake, BigQuery, Databricks, and Postgres, modeled for your scale.
Data engineering services
Your data, made reliable.
DataClerk designs, builds, and operates the data layer your business runs on — warehouses, pipelines, models, and governance. We clean up what's broken and engineer what's next.
Engagements
Services and typical scope
Each engagement starts with the problem, not the tool. Below are the workstreams we run most often, the pain each one solves, and a typical price range for context — not a quote.
| Service | Pain it solves | Typical price |
|---|---|---|
| Data Health Audit | Finds bad pipelines, duplicate data, and broken KPIs. | US$5k–25k |
| Executive Data Warehouse | A modern warehouse, stood up in 4–8 weeks. | US$30k–150k |
| AI Readiness Assessment | Gets your data ready for Copilot and LLMs. | US$10k–50k |
| Customer 360 | Unifies ERP, CRM, and ecommerce into one view. | US$50k–300k |
| Manufacturing Analytics | Production, inventory, and supplier dashboards. | US$30k–200k |
| Data Quality Monitoring | Continuous anomaly detection, always on. | US$2k–10k / month |
| Data Catalog & Lineage | Governance, ownership, and documentation. | US$20k–100k |
| Cost Optimization | Cuts your Snowflake, BigQuery, or Databricks bill. | Share of savings or US$20k+ |
| Legacy ETL Modernization | Replaces SSIS, Informatica, or Talend pipelines. | US$50k–500k |
| Fractional Data Architect | A senior architect, 1–2 days a week. | US$5k–20k / month |
Ranges are typical, drawn from past engagements of similar scope. Every project is quoted after a short scoping call.
Approach
How an engagement runs
Four phases, each ending in something you keep — a report, a design, running code, or a handover. We start where the pain is sharpest.
01
Audit
Map your data, find the breaks, and write down what is actually true.
02
Architect
Design the warehouse, the models, and the contracts that fit your business.
03
Build
Ship production pipelines, dashboards, and tests — with documentation.
04
Operate
Monitor, maintain, and hand it over so your team can run it.
Capabilities
What we build, day to day
The disciplines behind every engagement. We pick the right depth of each for your problem — not all of them, every time.
Modeling
Dimensional models and metric layers in dbt, with tested contracts.
Pipelines
Batch and streaming ELT, CDC, and reliable schedules.
Quality
Tests, freshness checks, and anomaly detection that run on every load.
Governance
Catalog, lineage, access policies, and living documentation.
Analytics
Dashboards, trusted metrics, and self-serve for your teams.
Cost control
Warehouse tuning, query and storage savings, and budget guardrails.
AI readiness
Clean, documented data so Copilot and LLMs give useful answers.
Philosophy
“Clean data is not a project you finish. It is a property of the system you maintain.”
How DataClerk works
Operating principles
How we work with you
A few rules we keep on every engagement, so the work survives after we leave.
We always
- Ship production code with tests and documentation.
- Define the metrics and contracts before the dashboards.
- Hand over to your team — we do not hold the keys.
We never
- Recommend tools you do not need.
- Build pipelines that depend on us to run.
- Leave a mess behind.
Reach out
Tell us what's broken
Tell us about your data, your stack, and what's broken, missing, or next. We reply personally and send a short plan — a fixed quote for a project, or a partnership if the work is ongoing.
- 01You tell us what's broken or what you want to build.
- 02We scope the work and send a fixed quote or a partnership plan.
- 03We start with an audit or a pilot, then build to production.
We read every message personally and reply within a few days. This form is for starting a conversation — it does not create an account.