Engineering™ · Data & Intelligence

Data Governance & Architecture

We design secure, reliable corporate data architectures engineered for enterprise scale and audit compliance.

Data governance · End-to-end lineage · Security & compliance · Enterprise data catalog. We establish policies, role-based access controls, and technical architectures to ensure your data is trustworthy, traceable, and private.

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Scope

Data Governance Framework · End-to-End Data Lineage · Role-Based Access Control (RBAC) · Compliance & Privacy (GDPR/LGPD)

Estimated Timeline

6–10 weeks

Platforms

dbt Docs · Datafold / Monte Carlo · AWS IAM / GCP Cloud IAM · Apache Atlas / OpenMetadata

Target Fit

Is this for your company?

This service is for you if

✓Employees have broad access to confidential billing or customer data due to lack of granular access policies.
✓Nobody in the company knows where reported figures originate or what transformations they underwent (lack of data lineage).
✓You must comply with data privacy regulations (GDPR, LGPD, or local financial data regulations).
✓Your databases suffer from duplicate records and inconsistent metric definitions with zero designated Data Stewards.

You probably do not need it if

✕Your company has fewer than 15 employees and does not process sensitive customer PII or face regulatory oversight.
✕You only want a visual dashboard without implementing underlying security or quality controls.

Problem Space

What we solve

End-to-End Data Lineage & Traceability

Exact visual mapping tracing every metric from primary database write to final executive dashboard visual.

Role-Based Access Control (RBAC) & PII Masking

Strict Least Privilege security policies with automated hashing and masking of sensitive customer identifiers.

Enterprise Data Catalog & Metric Glossary

Centralized repository where every table, column, and business KPI has an approved definition and designated owner.

Regulatory Compliance & Audit Preparedness

Implementing data retention and privacy controls that satisfy international cybersecurity and legal frameworks.

Engineering Process

How it works

01 — Diagnose

Risk Assessment & Compliance Gap Analysis

We map sensitive data flows, evaluate existing access permissions, and identify regulatory compliance gaps.

02 — Design

Governance Framework & Access Policies

We design the RBAC permission matrix, data masking protocols, and standardized business entity catalogs.

03 — Build

Technical Implementation & Quality Controls

We enforce cloud IAM permissions, configure automated lineage tools, and implement data pipeline quality tests.

04 — Launch

Council Enablement, Training & Governance Handoff

We establish the internal data governance council, train data stewards, and deliver compliance audit documentation.

Deliverables

What we deliver

Upon completion you will have

✓Corporate data governance charter complete with documented roles, policies, and operating procedures.

✓Interactive, automated end-to-end data lineage map.

✓Centralized enterprise data catalog and standardized business metric dictionary.

✓Technical cloud RBAC configuration and automated PII data masking layers.

✓Formal compliance audit readiness report.

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:6–10 weeks

Weeks 1–2Phase 1

Access Audit & Sensitive Data Discovery

Permission inventory, identifying databases with PII, and regulatory risk assessment.

Weeks 3–5Phase 2

Policy Framework & Data Catalog Design

Governance manual drafting, RBAC matrix modeling, and corporate catalog structuring.

Weeks 6–8Phase 3

Technical Enforcement & Automated Masking

Cloud IAM policies rollout, column-level masking implementation, and automated lineage tooling.

Weeks 9–10Phase 4

Audit Simulation & Governance Handoff

Simulating regulatory compliance audits, training data stewards, and formal handover.

Pricing Guidance

Estimated Investment

Target Investment

USD 6,800

Includes security audit, governance framework design, enterprise data catalog, cloud RBAC configuration, and lineage mapping.

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Real-World Proof

Impact Case Study

From Audit Chaos to 100% Traceable Lineage and Bank-Grade Regulatory Compliance

Initial problem

Regional lending fintech facing imminent regulatory penalties due to inability to prove which employees accessed borrower data or how calculations were derived.

Technical intervention

Deployment of an enterprise governance framework with dbt Docs, BigQuery PII column-level masking, and strict RBAC permission models.

Outcome achieved

Passed the external regulatory audit with zero findings, reduced unauthorized sensitive data access to zero, and achieved complete calculation lineage.

Clarifications

Frequently asked questions

Does data governance slow down analytics velocity?

On the contrary. When data is governed with certified catalogs and clear definitions, analysts spend zero time debating number origins or filing manual access tickets. They work faster with absolute trust in underlying figures.

How do you handle sensitive customer PII (names, phone numbers, tax IDs)?

We configure automated column-level masking and cryptographic hashing in warehouse layers: analysts can calculate cohort stats and join records without ever exposing raw customer identifiers.

Does this service help us comply with privacy laws like GDPR or LGPD?

Yes. Our framework specifically addresses the right of access, rectification, pseudonymization, and deletion mandated by modern data protection regulations.

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