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Systems & data

Data Platform & Cloud Foundations

Build the data pipelines and cloud foundations behind your analytics and AI systems. I implement ingestion, transformation and usable data models, with validation, operational visibility and a handover your engineers can use.

Discuss this service

Is this the right service?

Who it is for. Data and engineering owners who need reliable source data for analytics, knowledge systems or production applications.

The problem. Fragmented ingestion, inconsistent transformations and unclear ownership make downstream systems difficult to operate.

The result. A documented path from source data to usable datasets, with owners and checks for each important boundary.

What it covers

Source and pipeline scope

Identify the datasets, owners, refresh requirements and downstream uses before choosing infrastructure.

  • Produce a source map and data-contract requirements.

Ingestion and transformation

Build the agreed pipelines with reproducible transformations and checks for incomplete or unexpected data.

  • Implement ingestion jobs, transformation code and validation checks.

Cloud and access foundations

Configure deployment and access for the client’s environment, with infrastructure changes recorded as code where appropriate.

  • Document permissions, configuration and deployment steps.

Operational handover

Exercise failed-job and stale-data cases so your team can recognize and investigate issues.

  • Deliver a data model, runbook and recovery examples.

Approach

  1. 01

    Scope the data

    Agree sources, consumers, update needs and acceptance checks.

  2. 02

    Build and validate

    Implement ingestion, transformations and the agreed cloud setup, then validate representative data.

  3. 03

    Hand over operations

    Review failed runs, ownership and deployment instructions with the operating team.

What you get

Pipelines and infrastructure code, documented data models, validation checks and operating instructions for an agreed set of sources.

  • Receive pipeline and infrastructure code with configuration instructions.
  • Review data validation results, model documentation and known limitations.
  • Use an operating runbook covering refreshes, failed jobs and recovery.

Tools and standards

Use the cloud and data tooling that fits the existing environment. My delivery experience includes AWS Glue, Lambda, Step Functions, Terraform, Databricks and Superset. The project scope determines which components are needed.

Relevant experience

For the Government of Canada, I built cloud data orchestration and production data-platform capabilities. This is evidence of hands-on platform engineering; the implementation scope for your source systems is agreed separately.

When the source material is documents that people need to query, explore private knowledge assistants.

When an application needs to retrieve evidence and prepare a reviewed result, explore agentic workflow automation.

If source readiness and project priorities are still uncertain, begin with an AI opportunity assessment.

Working together

Projects are delivered remotely, with a defined scope, scheduled reviews and written progress updates. We agree meeting times, access requirements and acceptance criteria before work begins. Code, documentation and handover are included as specified in your engagement.

Direct booking, no form required

Discuss this engagement

Bring the source systems, the downstream use and the pipeline problem your team needs to solve.

Book a 30-minute callor email info@yannickbounouar.com

Prefer to write? Send a project enquiry.