Postgraduate Diploma in Data Quality, Catalogue and Traceability
Online 800-hour Postgraduate Diploma in Data Quality, Catalogue and Traceability. 8 modules covering ISO/IEC 25012, ISO 8000-8 and Great Expectations. Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Understand the dimensions and measurement of data quality.
Profile datasets and define data quality rules.
Automate data validation in pipelines.
Implement data catalogues with business glossaries.
Capture and use lineage for impact analysis.
Design domain-based data governance models.
Apply privacy, integrity and compliance requirements.
Who it's for
- Data quality engineer looking to specialise in Data Quality, Catalogue and Traceability.
- Data steward looking to specialise in Data Quality, Catalogue and Traceability.
- Data governance lead looking to specialise in Data Quality, Catalogue and Traceability.
- Data engineer looking to specialise in Data Quality, Catalogue and Traceability.
- Metadata analyst looking to specialise in Data Quality, Catalogue and Traceability.
- Data architect looking to specialise in Data Quality, Catalogue and Traceability.
- Engineering or science graduates seeking to enter industry.
What you'll work with
- ISO/IEC 25012
- ISO 8000-8
- ISO/IEC 25024
- ISO 8000-61
- ISO/IEC 38505-1
- W3C PROV-DM
- Reglamento (UE) 2016/679
- 21 CFR Part 11
8 modules · 800 h
1.1Quality dimensions according to ISO/IEC 250121.2Accuracy, completeness and consistency1.3Timeliness, uniqueness and validity1.4Measurement according to ISO/IEC 250241.5Cost of poor data quality1.6Master and reference data1.7ISO 8000 series and master data1.8Owner and custodian roles1.9Data quality maturity1.10Case study: quality diagnosis of a data domain
2.1Column profiling and distributions2.2Outlier detection2.3Business rules and constraints2.4Schema validation2.5Deduplication and fuzzy matching2.6Normalisation and standardisation2.7Handling nulls and default values2.8Quality in sensor data2.9Dataset quality scoring2.10Practical: profiling with pandas and ydata-profiling
3.1Expectations with Great Expectations3.2Data testing in dbt3.3Data contracts between producers and consumers3.4Validation in streaming pipelines3.5Data quality gates in CI/CD3.6Quarantine and rejection management3.7Alerts and quality thresholds3.8Data observability3.9Quality reports for business3.10Lab: validation suite in a pipeline
4.1Types of metadata: technical, business and operational4.2Business glossaries4.3OpenMetadata and DataHub catalogues4.4Apache Atlas in Hadoop ecosystems4.5Asset discovery and search4.6Classification and tagging4.7Automated metadata collection4.8Integration with BI tools4.9User adoption of the catalogue4.10Workshop: deploying OpenMetadata
5.1Table-level and column-level lineage5.2OpenLineage standard5.3W3C PROV model5.4Lineage in Spark, Airflow and dbt5.5Change impact analysis5.6Batch traceability in manufacturing5.7Regulatory traceability in critical sectors5.8ALCOA+ data integrity5.9Auditing and reconstruction of results5.10Lab: capturing lineage with OpenLineage
6.1Governance frameworks and DAMA-DMBOK6.2Data governance under ISO/IEC 38505-16.3Policies, standards and procedures6.4Data council and committees6.5Master data management (MDM)6.6Data mesh and domain ownership6.7Data products and SLAs6.8Governance indicators6.9Organisational change management6.10Case study: domain-based governance model
7.1Classification of sensitive data7.2GDPR requirements and data minimisation7.3Pseudonymisation and anonymisation7.4Attribute-based access control7.5Data retention and deletion7.6EU Data Act7.7Data integrity in pharmaceuticals and 21 CFR Part 117.8Compliance audits7.9Record of processing activities7.10Practical: tagging personal data in the catalogue
8.1Selecting the data domain8.2Initial quality diagnosis8.3Defining rules and metrics8.4Implementing validations8.5Domain catalogue and glossary8.6Lineage capture and analysis8.7Proposed governance model8.8Improvement indicators8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100 % online training on the SEIUM Institute campus, with downloadable materials, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the enrolment period, making it possible to fit around work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Great Expectations, OpenMetadata, dbt, OpenLineage, Apache Atlas, in free, academic or evaluation versions. Tests are studied using real data quality audit procedures for a data domain, automated validation in a pipeline, and impact analysis through lineage. Assessment is continuous, module by module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Understand the dimensions and measurement of data quality.
Profile datasets and define data quality rules.
Automate data validation in pipelines.
Implement data catalogues with business glossaries.
Capture and use lineage for impact analysis.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical standards documentation.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours, spread over 6 to 8 months. That is an average of 10 to 15 hours a week, compatible with a full-time job.
You work with Great Expectations, OpenMetadata, dbt, OpenLineage and Apache Atlas. Free, academic or evaluation versions are used, and the tutor indicates a free alternative where one exists.
An own diploma from SEIUM Institute upon passing continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Check the recognition policy when you enrol.
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