Service — Data Platforms

From raw data to trusted insight.

Data pipelines, warehouses, and analytics layers engineered for scale and governance — ETL/ELT, real-time streaming, and dashboards — built AI-first, with senior engineers reviewing every line.

What is it? A data platform is the full path from source to insight that Kinisys builds for you — ingestion and ETL/ELT pipelines, a warehouse or lakehouse, transformation models, optional real-time streaming, and dashboards — engineered for scale and governance. AI agents do the building under senior engineering review, the code lives in your GitHub from commit one, and you own 100% of it.

What you get

  • ETL/ELT pipelines — reliable ingestion from your sources with transformation, modelling, and automated data-quality tests.
  • Warehouse or lakehouse — a scalable, well-modelled store that serves analytics and applications alike.
  • Real-time streaming — event pipelines for use cases that can't wait for a nightly batch.
  • Analytics & dashboards — the metrics layer and dashboards your teams use to make decisions.
  • Governance built in — schema, lineage, access control, and monitoring designed from day one, not bolted on.
  • Your repository — pipelines, models, and infrastructure-as-code in your GitHub organization, with handover docs and no lock-in.

How we build it

One focused workshop locks the scope and a fixed-price proposal. Senior engineers then design the data architecture, models, and governance plan — the blueprint the AI builds against. AI agents generate the pipelines, transformations, infrastructure-as-code, tests, and dashboards in parallel, and every pull request is reviewed line-by-line by a senior before it ships. You see weekly demos against a live staging environment, and every build includes a 30-day post-launch window for fixes and tuning.

Default technology stack

Python (FastAPI) and Node.js for services and pipelines, PostgreSQL plus Redis as core stores, hosted on AWS or Vercel, with Next.js / React / TypeScript dashboards and Supabase where it fits. An AI layer (OpenAI, Anthropic Claude, open-weights via LangGraph) is added where intelligence or enrichment adds value. If you have a hard requirement — Java, .NET, or on-prem — we adapt.

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FAQ

Data platform questions.

What does a data platform include?
Typically the full path from source to insight: ingestion and ETL/ELT pipelines, a warehouse or lakehouse, transformation and modelling layers, optional real-time streaming, plus the dashboards and APIs your teams and applications consume. Governance, lineage, and access control are built in rather than added later.
How do you handle data governance and quality?
We design schema, lineage, access control, and validation into the pipelines from day one, with automated data-quality tests and monitoring. Senior engineers review every pull request, so governance is part of the build rather than a bolt-on afterthought.
Do I own the data platform code?
Yes — 100%. The pipelines, models, and infrastructure-as-code live in your GitHub organization from the first commit, with handover documentation. There is no license-back, no shared ownership, and no proprietary platform you must keep paying for.

Ready to build your data platform?

Tell us what data you have, what you want from it, your rough timeline, and any budget range. We reply within one business day.

Book a free consultation