---
title: "I Will build an ETL data pipeline that moves data between your systems reliably"
url: https://zinnhub.com/zinns/i-will-build-an-etl-data-pipeline-that-moves-data-between-your-systems-reliably/
description: "An ETL pipeline that moves your data between systems on schedule, handles failures without losing rows, and tells you when something breaks."
published: 2026-10-21T11:03:52+00:00
modified: 2026-10-21T11:03:52+00:00
language: en-GB
---

# I Will build an ETL data pipeline that moves data between your systems reliably

### What this is

A reliable pipeline between the systems you already run: database to warehouse, CRM to reporting, API to spreadsheet, or all three. The job is not the happy path — every tool can copy rows once. The job is what happens on day forty when a token expires at 2am, a source adds a nullable column, or the network drops halfway through a batch.

### How I build it

Extract, load, and transform are separate steps, because they fail for different reasons and should be retried differently. Extraction records a cursor or watermark so a restart resumes instead of duplicating. Loading runs inside a transaction per batch, so a crash leaves the destination consistent rather than half-written. Transformation lives in versioned SQL or Python, not inside a visual builder where nobody can read it six months later.

Every run writes a manifest: rows read, rows written, rows rejected, and the reason for each rejection. Idempotency is enforced with a natural key plus a hash, so re-running yesterday’s job is harmless. Schema changes are detected before they break a load, and they alert rather than fail silently.

### What you get

- Pipeline code in your repository, with tests against a sample of real data.
- A scheduler: cron, systemd timer, Airflow or Prefect depending on your stack.
- Monitoring and alerting on row counts, failures and latency drifts.
- A runbook covering replay, backfill and how to add a new source.

### What I need from you

The source and destination systems with read credentials, one working example of the data, and the schedule you actually need rather than the one that sounds impressive.

### Turnaround and communication

First working pipeline usually within five days, with a staging run you can inspect before it touches production. I send a short written update at each milestone rather than a chain of meetings, and every decision that affects your data — retention, backfill window, what happens to rejected rows — is written down where you can find it later. If something cannot be made reliable in the time available, you hear it early enough to change the plan instead of discovering it at launch.