Staff Data Engineer - Governance Team - Blip

Why this Role Exists

At Blip , data doesn't just inform decisions—it powers experiences. But data at scale demands discipline: governance that enables (not blocks), costs that are predictable, and standards that accelerate (not slow down) teams.

We're migrating from Azure/Databricks to Google Cloud Platform with a clear mandate: build it right from day one. No legacy debt. No ungoverned pipelines. No cost surprises.

We're looking for a Staff Data Engineer with a rare blend: deep hands-on technical skills in data platforms plus strategic thinking in governance, cost optimization, and enablement. Someone who can turn policies into code, transform Google PSO artifacts into reusable templates, and scale best practices across domains—without becoming the "policy police."

Your Mission

You'll be the technical backbone of our Data & Analytics Center of Excellence (CoE), translating governance strategy into automation, patterns, and tooling that make "doing the right thing" the fastest and easiest path for data teams.

You'll work hand-in-hand with our Data Architect and Google Cloud Professional Services to:

• Implement governance-as-code
• Embed FinOps discipline into every pipeline, dataset, and model from day one.
• Build reusable templates, accelerators, and frameworks that scale across domains
• Enable federated governance: empowering teams with self-service tooling, guardrails, and observability.

Key Responsibilities

Governance Engineering & Automation

• Design and implement governance-as-code frameworks using GCP-native tools
• Build automated workflows for data classification, lineage tracking, quality validation, and compliance reporting (LGPD, GDPR, ISO 27001).
• Create reusable Infrastructure-as-Code (IaC) modules (Terraform) for governance foundations: project structure, IAM roles, security policies, audit logging, retention policies.
• Implement the Governance Seal framework: automated validation that datasets/pipelines meet minimum standards (quality, security, documentation, cost efficiency) before entering production.

FinOps for Data & Cost Optimization

• Embed FinOps best practices into CoE templates: partitioning strategies, clustering, storage classes (Standard/Nearline/Coldline/Archive), lifecycle policies, query optimization, slot reservations, autoscaling.
• Build cost monitoring and alerting for GCP data services.
• Identify, remediate and direct cost waste: unused tables, redundant pipelines, inefficient queries, over-provisioned resources.

CoE Platforms, Templates & Accelerators

• Transform deliverables (TDDs, IaC modules, pipeline patterns, KT sessions) into production-ready CoE standards: documented, versioned, tested, and reusable.
• Build a catalog of pipeli