Data Engineer - Governance Team

Overview

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."

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:

• Embed Fin Ops 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.

• 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.

• Embed Fin Ops 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.

• Transform deliverables (TDDs, IaC modules, pipeline patterns, KT sessions) into production-ready CoE standards: documented, versioned, tested, and reusable.
• Build a catalog of pipeline patterns and templates for common use cases: batch ingestion (JDBC, APIs, files), streaming (Pub/Sub, Kafka), CDC, data contracts, gold layer serving (fast + analytical).
• Develop self-service toolkits for Data Stewards and domain engineers: project setup scripts, data contract generators, quality rule templates, lineage visualization, metadata enrichment.
• Establish CI/CD patterns for data pipelines: automated testing (schema validation, data quality, security checks), deployment gates (Governance Seal validation), rollback strategies.

• Design reference data models that embed governance from the start: clear ownership, semantic definitions, access policies, retention rules, lineage traceability.
• Optimize storage and compute for both performance and cost: partitioning, clustering, indexing, compression (Parquet, Avro), Delta/Iceberg table formats.
• Define standards for data contracts (schemas, SLAs, ownership, change management) and implement them as code (Avro schemas, Protobuf, dbt contracts, data validation frameworks).

• Implement end-to-end data lineage (Dataplex, Open Lineage, custom metadata extraction) from source systems to analytics/AI consumption.
• Build monitoring and alerting for governance KPIs: policy compliance, data freshness, quality SLA breaches, access anomalies, cost overruns.
• Define and enforce SLAs/SLOs for data delivery, accuracy, and availability—across raw, trusted, and gold layers.
• Act as technical…