Data Engineering

Overview

A model is only as good as what it reads. We map the data a product needs before it is built, and fix the gaps first.

Pipelines are tested like code. When data is late, missing or wrong, someone is told before a report or a model uses it.

Who it is for

• Companies whose data sits in many systems and spreadsheets.
• Teams preparing data for an AI product or a retrieval system.
• Analytics teams that spend more time fixing data than using it.

What you get

• Ingestion pipelines from your applications, files and third parties.
• Warehouses and lakehouses on the cloud you already use.
• Data quality checks that fail loudly, not silently.
• Access controls and lineage so you know who used what.
• Feature and vector stores for models and retrieval.
• Documentation of every table a person or a model relies on.

How it runs

1. Audit: sources, owners, quality and the questions the data must answer.
2. Design: target model, pipelines and access rules.
3. Build the first pipelines for the most valuable use case.
4. Add quality checks, monitoring and documentation.
5. Extend source by source.

In practice

• One customer view built from CRM, billing and support data.
• A finance data platform that reconciles bank, ledger and payment-gateway data every day.
• Document collections cleaned and indexed for a knowledge assistant.
• Daily operations dashboards that no longer need manual exports.

Technology

• Snowflake, BigQuery, Databricks or PostgreSQL.
• dbt, Airflow and cloud-native pipeline tools.
• Kafka or cloud queues for streaming data.
• pgvector and managed vector stores for retrieval.

How we charge

A fixed-fee audit, then fixed-scope builds per use case, or a monthly data team.

Questions