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Data & analytics hub

Warehouses, BI, and ML ops—measure before you mythologize dashboards.

How we score: Methodology.

Analytics engineer

Pipelines, modeling, and sane SQL defaults.

Data scientist

Notebooks, GPUs, and experiment tracking.

BI / business analyst

Self-serve metrics with governance.

ML platform leaning

Observability for models in production.

Search & RAG apps

Vectors next to relational truth.

API & GraphQL data layer

Federation and caching at the edge of your graph.

Realtime & events

Streams, caches, and fan-out.

Governance & catalogs

Lineage and access policies as tables multiply.

Startup data stack

Managed warehouses and honest pricing.

Data career paths

Role boundaries and interview focus areas.

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