ODI ETL Automation, Data Quality Guidance
Budget: £10 – £15 GBP
My current workload centers on Oracle Data Integrator (ODI) projects that pull data from APIs and other cloud services into Oracle Cloud Infrastructure. I need practical, hands-on support to strengthen three areas:
1. Building a reusable Oracle Data Quality (DQ) Framework that plugs neatly into OCI Data Integration.
2. Automating the ETL workflow in ODI—from source ingest through load—so every run is fully tested without manual intervention.
3. Embedding robust data-validation checkpoints throughout the pipeline to ensure accuracy, completeness, and early error detection.
You will be working exclusively in the ODI ecosystem, orchestrating API-driven loads, and codifying data-quality rules that can be triggered automatically. Reusable test cases, clear logging, and easy-to-maintain scripts are priorities; I want to spend time analysing results, not chasing failures.
Deliverables I can immediately act on:
• Parameter-driven ODI scenarios or packages that execute the full API-to-target flow and include automated rollback on failure.
• A library of DQ rules (referential integrity, format, range, custom API response checks) integrated into the above scenarios.
• Documentation and walkthrough explaining how to extend the framework for future sources and how to schedule it in OCI Data Integration.
Acceptance criteria:
• All jobs complete in a single touchless execution with pass/fail reporting.
• DQ rules run against at least one live API source and one target table, with results stored in an audit table.
• I can clone the provided artifacts to a new project without code changes other than parameter values.
If you’re comfortable scripting in ODI, automating test cycles, and hardening data quality on cloud-based sources, your expertise will fit perfectly here.
1. Building a reusable Oracle Data Quality (DQ) Framework that plugs neatly into OCI Data Integration.
2. Automating the ETL workflow in ODI—from source ingest through load—so every run is fully tested without manual intervention.
3. Embedding robust data-validation checkpoints throughout the pipeline to ensure accuracy, completeness, and early error detection.
You will be working exclusively in the ODI ecosystem, orchestrating API-driven loads, and codifying data-quality rules that can be triggered automatically. Reusable test cases, clear logging, and easy-to-maintain scripts are priorities; I want to spend time analysing results, not chasing failures.
Deliverables I can immediately act on:
• Parameter-driven ODI scenarios or packages that execute the full API-to-target flow and include automated rollback on failure.
• A library of DQ rules (referential integrity, format, range, custom API response checks) integrated into the above scenarios.
• Documentation and walkthrough explaining how to extend the framework for future sources and how to schedule it in OCI Data Integration.
Acceptance criteria:
• All jobs complete in a single touchless execution with pass/fail reporting.
• DQ rules run against at least one live API source and one target table, with results stored in an audit table.
• I can clone the provided artifacts to a new project without code changes other than parameter values.
If you’re comfortable scripting in ODI, automating test cycles, and hardening data quality on cloud-based sources, your expertise will fit perfectly here.