Ingestion
Batch or streaming ingestion from your existing sources into S3.
AWS data engineering: pipelines, ETL and data quality
Data becomes useful when it is structured, validated and attributable to a source. Exports and files that nobody can trace are not a data platform.
I build AWS data pipelines: ingestion from your sources, orchestrated ETL, quality controls and datasets ready for analytics or AI, with cost measured per pipeline.
The situations this answers
- Manual exports and files that sleep on a share or in a bucket.
- Pipelines that break as soon as volume or sources change.
- No quality checks, no alerting and no clear owner for a failed run.
- Cloud cost that cannot be attributed to a pipeline or a team.
The solutions I bring
- Sources
- Ingestion
- Transformation
- Quality gate
- Data lake and databases
Orchestration · monitoring · cost per pipeline
Orchestrated ETL
Idempotent pipelines with retries and explicit boundaries between stages.
Quality checks
Schema, completeness and range checks stop bad data before it reaches consumers.
Analytics-ready data
Documented datasets in your data lake or database, ready for analytics and AI.
What you get
- Ingestion from your existing sources, batch or streaming.
- Orchestrated ETL with retries, idempotency and explicit boundaries.
- Quality checks and alerting before bad data reaches consumers.
- Datasets ready for analytics and AI, with documented schemas.
- Cost measured per pipeline, not discovered at the end of the month.
Commercial proposal
Data and flow audit
Inventory the sources, volumes, freshness requirements and current failures before choosing any tool.
Pipeline build
Implement ingestion, transformation and quality checks, then test them on real volumes and real edge cases.
Industrialisation and handover
Add orchestration, monitoring, cost controls and documentation, then hand the runbook to your team.
Frequently asked questions
Where should a data engineering project start?
With the sources and the questions the data must answer. The audit maps volumes, freshness and failures, then defines the smallest pipeline that delivers a usable dataset.
Batch or streaming?
Batch is usually enough and cheaper for reporting and document processing. Streaming is kept for cases where freshness changes the business decision.
How is data quality guaranteed?
Each pipeline runs explicit checks on schema, completeness and ranges. Failures stop the pipeline or flag the batch before it reaches consumers.
Who operates the pipeline after delivery?
Your team. Pipelines are versioned, documented and monitored, and a supervised period runs before the handover.
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