Staff Data Engineer - Governance Team

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 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.

Data Modeling & Storage Design (Governance Lens)

• 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).

Observability, Quality & Reliability

• Build data quality checks
• Implement end-to-end data lineage (Dataplex, OpenLineage, 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.

Enablement & Leadership

• Act as technical mentor and sparring partner for Data Stewards, domain engineers, and analytics teams adopting CoE standards.
• Lead design reviews and architecture spikes for governance, FinOps, and platform patterns.
• Produce playbooks, runbooks, and hands-on labs: "How to set up a governed project in GCP," "FinOps checklist for BigQuery," "Implementing dynamic data masking," etc.
• Facilitate Community of Practice sessions: share patterns, troubleshoot blockers, celebrate wins, iterate on feedback.

What We’re Looking For

Experience

• 6+ years in Data Engineering, Platform Engineering, or Data Infrastructure roles, with at least 2 years focused on governance, data quality, or platform enablement.
• Proven experience implementing data governance frameworks in production: metadata management, access control, data quality, lineage, compliance.
• Hands-on experience with FinOps for data platforms: cost monitoring, optimization, chargeback/showback models, budgeting.
• Experience building reusable frameworks, templates, or internal platforms that other engineers consume (not just one-off pipelines).

Technical Foundations

• GCP expertise (required): BigQuery (IAM, policies, partitioning, clustering, masking), Dataplex (lakes, zones, catalog, lineage, quality), Cloud Storage, Pub/Sub, Dataproc, Composer/Airflow, Cloud DLP, IAM.
• Infrastructure-as-Code: Terraform (or equivalent) for provisioning and managing GCP data infrastructure at scale.
• Programming: Strong Python and SQL; ability to write clean, testable, maintainable code for data pipelines and automation.
• Data modeling: Deep understanding of dimensional modeling, event sourcing, CDC, slowly changing dimensions, data contracts, schema evolution.
• CI/CD for data: Experience with automated testing, deployment pipelines, version control (Git), and release management for data products.
• Data quality & observability tools: Great Expectations, dbt, Dataplex Data Quality, OpenLineage, or similar.

Governance & Compliance Knowledge

• • Understanding of data privacy regulations (LGPD, GDPR) and how to implement technical controls: data classification, masking, anonymization, retention, audit trails.
  • Experience with Data Loss Prevention (DLP) tools and techniques: scanning, tokenization, redaction, de-identification.
  • Familiarity with Data Mesh or federated governance models: domain ownership, data as a product, self-serve platforms, governance as guardrails (not gates).

Bonus Points

• Experience with Azure/Databricks → GCP migrations, especially governance and cost remediation during migration.
• Knowledge of ML governance: feature stores, model lineage, experiment tracking, model monitoring, responsible AI practices.
• Contributions to open-source data governance or quality frameworks (Great Expectations, dbt packages, OpenLineage, Amundsen, Datahub, etc.).
• Experience with FinOps certification or formal FinOps programs (cloud cost management, budget forecasting, optimization strategies).
• Background in compliance audits or enterprise data governance programs (ISO 27001, SOC 2, PCI-DSS).

Why Join Us

You'll join a company with global ambitions and a highly motivated Data team at a critical inflection point: migrating to a modern cloud stack and building the governance, cost discipline, and enablement culture that will scale for years to come.

Here, you won't just implement tools—you'll architect the foundations of how Blip governs, optimizes, and democratizes data. You'll work directly with Google Cloud Professional Services, shape corporate standards, and enable dozens of engineers and analysts to build faster, safer, and smarter.

This is a rare opportunity to be the "governance engineer" who makes governance loved, not feared.

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