Senior Data Architect

Há 6 horas

Rio de Janeiro, Rio de Janeiro, Brasil beBeeData Architect Tempo inteiro US$57.264 - US$120.384
Job Overview

We are seeking a skilled professional to lead our Data Architecture initiatives. This role involves owning the design and governance of our Lakehouse platform and representing that vision to clients.

Main Responsibilities
  • Client Engagement & Solution Development
    • Act as the primary technical liaison during key engagements, translating business goals into architecture that both sides understand.
    • Lead discovery workshops and roadmap sessions to surface requirements, constraints, and success metrics, then map them to scalable patterns.
    • Partner with account & sales teams to shape estimates, reference architectures, and bill-of-materials for proposals and Statements of Work.
    • Provide architecture-level answers for Requests for Proposal/Requests for Information and join pitch calls when deep credibility is essential.
    • Mentor client technical leads during early project phases to ensure knowledge transfer and long-term success.
  • Lakehouse Architecture & Design
    • Design logical/physical models, storage layers, and streaming/CDC patterns with Delta Lake and Unity Catalog.
    • Architect multi-cloud Databricks solutions covering ETL/ELT, structured streaming, and governance zones.
  • Governance & Security
    • Define catalog/permission models, retention policies, and lineage artifacts to meet HIPAA, SOC 2, GDPR, and similar frameworks.
    • Implement row-/column-level security, tokenization, and end-to-end audit logging.
  • Performance & Cost Optimization
    • Tune cluster sizing, Photon/SQL Warehouse configs, Z-Ordering, and auto-compaction to hit SLA and cost targets.
    • Instrument dashboards for query latency, job runtimes, and spend.
    • Lead design reviews, pair with engineers on PySpark/Scala, and sign off on pull requests before production.
    • Publish best-practice templates, Terraform workspace bootstraps, and CI/CD guidelines.
  • Cross-Functional Collaboration
    • Work closely with Platform Ops, Security, Analytics, and Product teams to translate requirements into production-ready data solutions.
    • Host lunch-and-learns and brown-bag demos to level-up Databricks skill sets across the organization.

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