Data Governance · Data Management · Data Privacy · Data Quality · AI Governance

Hands-on design and implementation of governed data foundations for AI, with a business-focused approach.

Multidisciplinary background spanning Software Engineering, Data Architecture, Data Operations, Data Management, Data Privacy & Compliance, AI Implementation, Program & Project Management, and Technology Leadership.

Open to senior Data & AI Governance roles and long-term embedded engagements with organizations across the US and LATAM.

• Data contracts & governance as code: machine-readable agreements and executable controls
• Data quality: dbt, Soda Core
• Privacy by design: sensitive data handling, anonymization and access control
• Metadata, catalog & lineage: OpenMetadata, dbt, Microsoft Purview, Databricks Unity Catalog

Frameworks: DAMA-DMBOK · DCAM · ISO 8000 · ISO/IEC 42001 · ISO/IEC 27001 · NIST AI RMF · EU AI Act · GDPR · CCPA

• Data: SQL, Python, Pandas, SQLAlchemy, dbt, Airflow, Airbyte, Data Warehouse, Data Lake, Lakehouse
• Data contracts: Open Data Contract Standard (ODCS), Data Contract CLI
• Data quality & governance: Soda Core, Microsoft Purview, Databricks Unity Catalog
• Cloud & infrastructure: AWS, Terraform, Docker
• Tooling: Git, GitHub, Linux, Shell scripting

Reproducible OpenMetadata implementation that turns a digital commerce warehouse into governed, AI-ready data context.

• Metadata ingestion, profiling, ownership, domains, glossary, classifications and Data Product curation
• dbt descriptions, tests, tags and table/column lineage consolidated in OpenMetadata
• API-derived AI Context Pack containing governed metadata without customer rows

Reference implementation that turns data contracts into executable governance controls for regulated analytics and AI.

• Three governed boundaries: telecom events → behavioral features → explainable credit decisions
• CI gates for ODCS conformance, organizational policy, compatibility and runtime data quality
• Dockerized PostgreSQL demonstrations with passing and intentionally rejected scenarios

Production-oriented implementation of executable data-quality controls for fintech payments.

• Four Soda Core contracts across payment events, transactions, ledger entries and settlements
• Nine deterministic scenarios covering a passing path and intentionally rejected data-quality failures
• Reproducible local execution with DuckDB and Parquet, plus CI-generated quality evidence

MLD is a method for exploring a technology through a small working deployment of its essential behavior.

• Quickly explore a technology and understand how it works through a run → change → observe → understand loop
• Human-defined learning goal and agent-built lab, guided by reusable AGENTS.md and MINIMUM_LAB_DEPLOYMENT.md instructions
• Eight-part mld/ guide covering fundamentals, a hands-on tutorial, use cases, references, practices and next iterations

ISO 27001 Internal Auditor · AWS Certified Solutions Architect – Associate · AWS Certified Data Engineer – Associate

• LinkedIn: https://www.linkedin.com/in/gregoriohakimian/
• Website: https://www.vektoria.io