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Data Engineering

Data engineering is unglamorous and decisive: every dashboard, report, and AI system your company runs stands on it. We build pipelines and platforms that move data reliably, observably, and at a cost you can explain — the practice behind a pipeline that went from six hours to thirty minutes on billions of records.

What You Get

Data that arrives on time, correct, every run.

Reliability you can point to

Pipelines with quality checks, retries, and alerts — when data breaks, you hear it from the platform, not from an executive’s dashboard.

Performance engineered, not hoped for

Distributed processing and workload design sized to your volumes — the difference between a 6-hour window and a 30-minute one.

Costs tied to value

Storage and compute choices made deliberately, with the expensive patterns (full scans, idle warehouses) designed out.

Built for the next system

Modeled, documented data that reporting and AI can build on — not a pile of one-off extracts that only their author understands.

Scope

What’s included.

  • Pipeline design & build — batch and streaming ingestion from your source systems
  • Warehouse / lakehouse modeling — schemas where each business concept means one thing
  • Data quality framework — checks, contracts, and alerting on every critical feed
  • Performance work — partitioning, clustering, and query patterns that cut runtime and cost
  • Orchestration & observability — schedules, lineage, and run visibility in one place
  • Documentation & handover — your team can run and extend what we build

At a Glance

  • Method: Proof-First Build
  • Headline result: Pipeline runtime 6h → 30min, billions of records
  • Platforms: Google Cloud and AWS data stacks
  • Typical scope: 4–8 weeks per platform phase
  • Ownership: Your accounts, your repositories
FAQ

Questions about this service.

Our pipeline is slow and fragile. Rebuild or repair?
The Blueprint answers that with evidence: we profile where time and failures actually concentrate. Often the honest fix is targeted — a few redesigns, not a rewrite. When a rewrite is right, we’ll show you why.
Can you work with our existing warehouse?
Yes — BigQuery, Snowflake, and the major AWS data services are home ground. Platform changes are recommended only when the evidence demands them.
How do quality checks avoid becoming noise?
Checks are tiered by consequence: block-the-load for data that corrupts decisions, warn-and-log for the rest. Every alert names an owner and a runbook entry.
Do you do real-time / streaming?
Where the business needs it — most don’t. We’ll show you the cost difference between streaming and a 15-minute batch before you choose.
Related

Often paired with.

Reporting & Analytics

The layer leadership actually sees, on top of these pipelines.

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Cloud Cost Optimization

Data platforms are usually the biggest cloud line item — audit it.

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Knowledge Assistants

Clean, governed data is what assistants answer from.

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Start with a fit call.

Thirty minutes. Bring the problem as it actually is — the stalled pilot, the bill, the spreadsheet everyone quietly maintains. We’ll tell you honestly whether we’re the right builder for it.

You leave with a straight answer on fit — and if it fits, a free Blueprint to follow.

Request a fit call How Proof-First Build works