Data Governance · Data Quality · Metadata Management

Make the complex traceable.

More than six years working with technical data, structuring information, solving data quality issues and automating processes so data remains consistent, traceable and usable.

Areas of expertise

01

Data Governance

Define standards, ownership and controls so data remains understandable, traceable and governable.

02

Data Quality

Detect inconsistencies, normalize information and make data quality visible and actionable.

03

Metadata & Lineage

Document context, relationships and provenance to understand where data comes from and how it is used.

04

Reference Data & Automation

Manage references, equivalences and lifecycle changes while automating data controls with Python and SQL.

How I work

This is how I turn heterogeneous data into a governed, traceable, and verifiable asset.

OPEN SCENARIOcase state
CHANGES / DRIFTsource change
CONNECTIONSdata between stagestap to explore
FLOW
OUTCOME
  1. Heterogeneous sources

    I inventory sources, structures, and incoming conditions to understand what I receive, detect incompatibilities, and decide what must be resolved before the data can be governed.

    • SQL
    • Python
    • API / Files
  2. Profiling and diagnosis

    I profile types, nulls, duplicates, cardinality, and patterns to measure the real state of the data, identify anomalies, and prioritize what must be corrected before rules are applied.

    • Pandas
    • SQL
    • Profiling
  3. Normalization and quality

    I standardize formats, reconcile duplicates, and apply quality rules; exceptions that cannot be resolved safely remain explicitly identified for review.

    • Python
    • SQL
    • DBT
  4. Lineage and traceability

    I record sources, transformations, and dependencies to reconstruct how each asset is produced, assess the impact of changes, and retain auditable evidence.

    • OpenLineage
    • DBT
    • Lineage
  5. Ownership and metadata

    I assign accountability, definitions, domain, and classification to turn a technical dataset into an asset that business and technology can understand and govern.

    • Collibra
    • Purview
    • Glossary
  6. Governance model and rules

    I translate business context into explicit policies and rules for identity, quality, consent, accountability, and use, defining what must be validated before the asset is published.

    • Data Contracts
    • Git
    • Policies
  7. Controls and automation

    I turn rules into executable validations, automate their application, and retain reproducible evidence to detect violations and manage them consistently.

    • Python / SQL
    • Bash / PS
    • CI/CD
  8. Monitoring and alerts

    I monitor quality, freshness, controls, and SLAs to detect deviations and trigger alerts before confidence in the asset is compromised.

    • DQ Metrics
    • Alerts
    • SLAs
  9. Governable data

    I publish an asset with accountable ownership, context, lineage, classification, controls, known quality, and verifiable usage restrictions.

    • Catalog
    • Lineage
    • Evidence