Jobs · Engineering

Lead Data Architect AI/ML (W2)

Snowrelic Inc · United States · 4 mo ago
RemoteRemoteEngineeringContract

Responsibilities

  • Data & AI Architecture Leadership
  • Design and evolve enterprise-scale data platforms and AI architecture
  • Enable analytics, ML, and intelligent automation across the division
  • Architect distributed data systems in microservices and event-driven environments
  • Strategy & Roadmapping
  • Define data modernization and AI strategy aligned to business goals
  • Develop long-term roadmaps for MLOps, governance, and AI enablement
  • Translate business strategies into measurable data solutions
  • AI & ML Enablement
  • Lead AI prototypes, POCs, and production of ML pipelines
  • Operationalize models using containers, APIs, and serverless frameworks
  • Establish CI/CD for ML and model observability practices
  • Data Engineering & Modeling Standards
  • Oversee ETL/ELT architecture from source to target
  • Define logical and physical data modeling standards (dimensional, normalized, denormalized)
  • Support BI, analytics, and AI data access patterns
  • Governance & Responsible AI
  • Establish data quality, lineage, and trust frameworks
  • Promote ethical AI, explainability, and regulatory compliance
  • Implement observability and governance best practices
  • Leadership & Influence
  • Guide architecture across Agile Release Trains (ARTs)
  • Influence cross-functional stakeholders and enterprise architects
  • Mentor engineers and data professionals

Qualifications

  • 5+ years leading enterprise data architecture initiatives
  • Expertise in distributed data systems and lakehouse/warehouse architecture
  • Experience with ML operationalization frameworks (MLflow, SageMaker, similar)
  • Strong Python, SQL, Snowflake, MongoDB skills
  • AWS cloud experience (Glue, Redshift, SageMaker, Bedrock preferred)
  • ETL/ELT design and implementation experience
  • Strong logical and physical data modeling expertise
  • Experience in microservices and event-driven architecture
  • Knowledge of data governance, explainability, and responsible AI
  • Experience in CI/CD for ML, data observability, and lineage tools
  • TOGAF Certification
  • Demonstrated strategic vision, roadmapping, and executive influence

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