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Builtwell

Service

Data Engineering & Automation

Scrapers, migrations, pipelines, and workflow automation. The plumbing that makes your data actually usable.

Timeline

3-8 weeks per pipeline

Pricing

Fixed fee per pipeline · ongoing platform retainer

What you get

  • ▸ETL/ELT pipeline design and build
  • ▸Data warehouse setup (BigQuery, Snowflake, Postgres)
  • ▸Scraper and ingestion services
  • ▸Workflow automation (n8n, Temporal, custom)
  • ▸Data quality monitoring + alerting

Who this is for

Your data lives in five tools that don’t talk. Or your team is exporting CSVs to make decisions. Or you have a manual process eating hours per week that should obviously be a script.

How we run it

We start by mapping the actual flow: where data is born, where it’s consumed, and where it gets lost. Then we engineer the missing connective tissue. Idempotent jobs. Type-safe schemas. Alerts that fire before the dashboard goes wrong.

We pick the boring, durable stack: scheduled jobs over event meshes when scheduled is enough, Postgres before a warehouse, Temporal/n8n before a custom orchestrator. Complexity costs.

What you get

  • A pipeline that runs unattended and tells you when it doesn’t
  • Documented schemas your analysts can trust
  • Backfill and replay tooling so a bad day doesn’t become a bad week
  • A runbook for the next person who has to touch it

Outcomes our clients see

  • Hours per week reclaimed from manual data ops
  • Reporting accuracy that survives a tool migration
  • A foundation that ML and AI work can actually run on

Outcomes

Numbers our clients see.

3-8 wk
Per pipeline, scope to ship
3-5×
Manual ops replaced per engagement
100%
Idempotent jobs, type-safe schemas

How we run it

A repeatable engagement.

  1. 01

    Source + sink audit

    We map every system the data has to flow through, what owns it, and what breaks today. You get a written assessment in week one.

  2. 02

    Schema + contract design

    Type-safe schemas, explicit data contracts, idempotency built in. So a re-run never duplicates and a schema change never silently corrupts downstream.

  3. 03

    Build with monitoring

    Postgres before warehouse, Temporal/n8n before custom orchestrator. Data quality checks and alerts wired in from the first job.

  4. 04

    Handoff + on-call

    Runbooks, dashboards, and a defined on-call window. Your analytics and ops teams can troubleshoot without paging an engineer.

FAQ

Common questions.

Do we need a data warehouse?
Not always. We'll start with Postgres if your scale doesn't justify Snowflake or BigQuery yet, and migrate later when there's a real reason. Cost and complexity discipline matter.
Can you work with our existing pipelines?
Yes. Most engagements start by stabilizing what you have , fixing idempotency, adding monitoring, plugging gaps , before building anything new.
Do you handle scrapers and third-party data?
Yes. Scrapers, vendor APIs, webhook ingestion, and reverse-ETL are all in scope. We build them with the same idempotency and monitoring discipline as the rest of the pipeline.
Who runs it after handoff?
Your team, with our docs and dashboards. We offer an ongoing platform retainer for shared on-call, schema evolution, and new pipelines as your data needs grow.

Ready to start a Data Engineering & Automation engagement?

Schedule a quick clarity call. We'll talk through your goals and where the leverage is, no slide deck, no pitch.

On the call we'll cover:

  1. 01 What you want to achieve and what success looks like
  2. 02 Where the leverage is in your current setup
  3. 03 Whether Data Engineering & Automation is the right place to start
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