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Leega

Senior Data and Agent Platform Engineer, GCP

Leega

. Build and maintain batch and streaming data ingestion, transformation, and quality pipelines using BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage .

Posted 11/9/2026full-timeRemote • BrazilSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in GCP data engineering, focusing on building and maintaining data ingestion and transformation pipelines while ensuring compliance with LGPD regulations. Proficient in utilizing tools like BigQuery, Dataflow, and Terraform to optimize data processes and support AI applications.

Highest-signal resume keywords
GCP Data EngineeringBigQueryDataflowPythonSQL

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data IngestionData TransformationData Quality PipelinesData GovernanceData ModelingMLOpsCI/CDTerraformEvent-Driven SystemsData Preparation for LLM
Soft Skills
Clear CommunicationStrong Security DisciplineEthical JudgmentTechnical Trade-Off ArticulationDocumentation Habit
Tools & Technologies
Cloud StoragePub/SubCloud ComposerCloud IAMDataplexVertex AIOpenTelemetryCloud LoggingCloud MonitoringCloud Trace
Certifications & Qualifications
Google Cloud Professional Data EngineerProfessional Machine Learning EngineerFinOps Certified Practitioner
Industry Keywords
LGPD ComplianceData LakeLakehouseData Residency ControlsAudit TrailsMCP ServersVector StoresGraphRAGHL7 StandardsCloud Healthcare API

Tech Stack

Tools & technologies
AirflowBigQueryCloudGoogle Cloud PlatformPythonSQLTerraform

About the role

Key responsibilities & impact
  • Build and maintain batch and streaming data ingestion, transformation, and quality pipelines using BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage
  • Model and organize the Data Lake/Lakehouse in BigQuery and BigLake, including layers, partitioning, and cost and performance optimization
  • Define data contracts, including schemas, freshness, and SLAs, and monitor source quality and lineage
  • Prepare data for AI through cleaning, anonymization, chunking, and embedding generation for Vertex AI RAG Engine, Vertex AI Search, and vector stores
  • Ensure LGPD compliance when handling sensitive health data by applying masking, anonymization, policy tags, and data residency controls
  • Design cross-project data access using Cloud IAM, Dataplex, BigLake, and Analytics Hub, following the principle of least privilege and maintaining audit trails
  • Expose data and internal systems as agent tools through MCP servers and MCP Toolbox, supporting integration with Vertex AI Agent Engine and ADK
  • Maintain MLOps/LLMOps pipelines with Vertex AI Pipelines, including evaluation, guardrails with Model Armor, and monitoring of drift and response quality
  • Provision data and AI infrastructure as code using Terraform and CI/CD
  • Track costs by business area, project, and model using billing exports to BigQuery and labels
  • Implement end-to-end observability with OpenTelemetry, Cloud Logging, Cloud Monitoring, and Cloud Trace
  • Support resilience and disaster recovery strategies

Requirements

What you’ll need
  • Proficiency in Python and SQL
  • Strong experience in GCP data engineering, including BigQuery, Cloud Storage, Dataflow or Dataproc, and Pub/Sub
  • Experience orchestrating pipelines with Cloud Composer/Airflow, Cloud Workflows, or similar tools
  • Knowledge of data governance and security in GCP, including Cloud IAM, Dataplex, policy tags, and Cloud KMS
  • Knowledge of Terraform and CI/CD, including Cloud Build, GitHub Actions, or similar tools
  • Hands-on experience in multi-project GCP environments with organizations and folders
  • Experience with distributed and event-driven systems, including queues, retries, idempotency, and consistency
  • Experience preparing data for at least one production LLM application using RAG or tool-enabled agents
  • Knowledge of LGPD as applied to sensitive data, including anonymization and masking techniques
  • Nice to have: Vertex AI, Gemini, Model Armor, agent frameworks, MCP servers, vector stores, GraphRAG, HL7/FHIR standards, Cloud Healthcare API, Data Mesh, hub-and-spoke architecture, and GCP FinOps
  • Google Cloud Professional Data Engineer, Professional Machine Learning Engineer, or FinOps Certified Practitioner certifications are a plus
  • Clear communication with data scientists, AI teams, business stakeholders, and external partners
  • Strong security discipline and ethical judgment when handling sensitive patient data
  • Ability to articulate technical trade-offs
  • Habit of documenting and assigning ownership of pipelines, data sources, and tools across teams

Benefits

Comp & perks
  • Porto Seguro medical insurance, with the option to add a spouse and children
  • Porto Seguro dental insurance for employees and dependents
  • Profit Sharing and Results Program (PLR)
  • Childcare assistance
  • Alelo food and meal vouchers
  • Home office allowance
  • Partnerships with educational institutions, offering discounts and incentives for courses and degree programs
  • Certification support, including cloud certifications in GCP, Azure, AWS, and other platforms
  • Livelo points
  • TotalPass, with discounted gym plans for employees and family members
  • Mindself, supporting meditation and mindfulness
  • Ongoing professional development
  • Collaborative work environment