Managed talent · Data & AI Engineering : Data, analytics, and AI — built governed.
Data engineers, analysts, and AI, ML, and LLM engineers as managed seats — building pipelines, models, and AI features under ISO 42001-aligned guardrails, with a supervisor who owns scope, review, and documentation.
You are building on your data — analytics, machine learning, or LLM features — and need it left governed and documented.
What the seats deliver
- Pipelines that move data once and correctly, with lineage you can trace
- Dashboards and reports leadership can rely on
- Machine-learning and LLM features with evaluation, logging, and a human override
- Model documentation produced as an artefact of the work, not an afterthought
- Data platforms on Snowflake, Databricks, and BigQuery kept tidy and costed
The specialisations in this practice.
AI & LLM
Machine learning
Data engineering & platforms
Analytics
Designed, evaluated, documented.
For data and AI work, supervision means a reviewed design before the build, evaluation criteria agreed upfront, and documentation that maps to ISO 42001 and the EU AI Act where they apply.
AI in production needs governance as well as engineering. See EU AI Act and ISO 42001 readiness.
Each seat has a fixed monthly price, dedicated or fractional, with a named Golonex supervisor. How managed talent is engaged.
Data & AI Engineering — your questions, answered
How is AI work governed? +
Every AI feature ships with a model card, an evaluation record, logging, and a human override path, scoped before the build starts. Where the EU AI Act or ISO 42001 applies, the documentation is written to map to it.
Does our data leave our environment? +
Not unless you agree to it. Engineers work inside your cloud accounts and data platforms with access you grant and can revoke. Where a third-party model is used, the data flows are documented and agreed before production data touches it.
Can you work with our existing data platform? +
Yes — Snowflake, Databricks, BigQuery, and the major cloud warehouses are all in scope. The supervisor starts by reviewing what exists and proposes changes, rather than rebuilding by default.
Tell us what you want your data to do.
A supervised seat proposal comes back within five business days.