Data Architect Lead

hace 5 días

Remote Peru Agentic Dream A tiempo completo

Agentic Dream is seeking a highly skilled Data Architect Lead. This transformative initiative focuses on unifying fragmented data systems into a centralized, AI-enabled data platform.

As a Data Architect Lead, you will play a key role in designing and delivering scalable, analytics-ready architecture that integrates data from 11+ ERP systems (NetSuite, Salesforce, SAP, QuickBooks, and more). This position demands strong technical acumen, hands-on leadership, and the ability to drive delivery across cross-functional teams, often under tight deadlines. You'll work in close collaboration with PMs, POs, and global stakeholders, primarily based in the U.S.

This is a high-impact opportunity for someone looking to architect data ecosystems that power enterprise-wide decision-making and AI-driven insights.

» Learn more about us at:

Key Requirements

Must-Have Qualifications

  • 8+ years in data architecture, data engineering, or similar roles within enterprise environments.
  • Proven experience integrating multiple ERP systems (NetSuite, SAP, QuickBooks, Salesforce, etc.).
  • Advanced proficiency in SQL, ETL design, and data modeling (star/snowflake schemas).
  • Strong command of cloud-based data platforms, especially Microsoft Azure.
  • Demonstrated expertise in Master Data Management (MDM) and data governance frameworks.
  • Experience deploying AI/ML models for reporting, forecasting, or anomaly detection.
  • Excellent English communication skills (C1 level), including stakeholder-facing experience.
  • Strong leadership in managing developers, analysts, and delivery teams.
  • Willingness to align working hours with U.S. EST and occasionally work extended hours.

Responsibilities

  • Architectural Design & Delivery: Lead the design and execution of data lakes, warehouses, and transformation pipelines across multiple ERP systems.
  • ETL Strategy: Define robust strategies to ingest, cleanse, harmonize, and model master data from fragmented sources.
  • Master Data Governance: Establish master data standards, naming conventions, validation rules, and enforce data quality protocols.
  • Data Modeling: Develop scalable enterprise models for sales, orders, inventory (SKU, BOM), and labor data.
  • AI Enablement: Support AI-driven tools for demand forecasting, margin analysis, and productivity insights.
  • Stakeholder Collaboration: Engage regularly with business users, PMs, and POs to align data architecture with business goals.
  • Team Leadership: Provide hands-on technical leadership to developers, analysts, and QA to ensure scalability, performance, and security standards are met.
  • Documentation & Delivery: Produce ERDs, transformation logic, and change logs for technical handoff and production deployment.
  • Workshop Facilitation: Conduct architecture reviews and stakeholder workshops to iterate and refine delivery approach.

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