A high-signal index of my public work across Data Engineering and Data Analytics.
Built around one principle: data must be trusted to be useful.
All datasets are synthetic / anonymized. No client data.
Go to Data Engineering → pick a demo that shows pipelines + quality gates + governance.
Go to Data Analytics → pick a repo showing semantic/KPI layer + security + storytelling.
Focus: integration, pipelines, reliability, data quality gates, governance-by-design.
- Qlik + Talend demos: https://github.com/wanascimento/qliktalend_demos
Hands-on demos: pipelines → quality checks → governed outputs.
de-data-quality-gates— profiling, rules, severity, exceptions, reportingde-lakehouse-medallion— bronze/silver/gold patterns + incremental loadsde-api-integration-patterns— retries, idempotency, batching, DLQ patternsde-link-analysis-demo— entity resolution + relationship graph (public-sector style)
Focus: consumption layer, semantic/KPI layer, security rules, decision storytelling.
- Qlik artifacts & references: https://github.com/wanascimento/qlik
Security rules, reusable assets, examples and references.
da-qlik-kpi-layer-starter— KPI model patterns + reusable measuresda-qlik-security-rules-examples— row-level security patternsda-qlik-storytelling-demo— narrative analytics for decision makers
Each mature repo follows this structure:
- Context: problem statement + constraints
- Architecture: diagram + key design decisions
- Data: synthetic dataset + dictionary
- Run it: reproducible setup (local and/or docker)
- Results: screenshots/outputs + key takeaways
- Trade-offs: limitations + what I’d change in production
- Refactor into 1 demo = 1 repo (high signal)
- Publish an anchor DE project with screenshots + walkthrough
- Add lightweight CI and quality checks where it adds value
https://www.linkedin.com/in/wmnascimento/
- LinkedIn: (add link)