Google Cloud Platform Data Architect

Strategic & Architectural Leadership

• Define and evolve AI & Data architecture strategy and roadmap, aligned with business priorities and IT strategy.

• Serve as a thought leader for modern data, analytics, and AI architectures, including Generative AI and Agentic AI.

• Identify, evaluate, and recommend emerging technologies, platforms, and architectural patterns.

• Partner with business and digital leaders to identify and prioritize high-impact AI and analytics use cases.

• Provide architectural guidance on ethical, responsible, and compliant AI adoption.

Solution Architecture & Platform Design

• Lead end-to-end architecture design for complex data, analytics, and AI initiatives, ensuring scalability, performance, security, and cost efficiency.

• Design and govern cloud-based data platforms leveraging:

  • Google Cloud Platform (BigQuery, Vertex AI, Dataflow, Dataproc, Looker)

  • AWS (S3, Glue, EMR, Redshift, SageMaker, Lambda)

  • Snowflake (data warehouse, data sharing, performance optimization)

• Architect modern enterprise data architectures, including:

  • Data Lake, Lakehouse, Data Mesh, and Data Fabric

  • Open table/file formats such as Parquet, Iceberg, Delta Lake

  • Medallion architectures (Bronze/Silver/Gold)

• Define data ingestion and integration patterns across structured and semi-structured sources (SAP, Oracle, Salesforce, JDE, Ariba, IoT, APIs, NoSQL).

• Define and enforce data quality, metadata, lineage, and access control standards.

AI, ML, and Generative AI Architecture

• Design and implement AI/ML and GenAI solution architectures from experimentation through production.

• Architect solutions for core ML use cases such as demand forecasting, predictive maintenance, supply chain optimization, and customer analytics.

• Lead architecture for Generative AI and Agentic AI, including:

  • LLM integration with tools, APIs, and knowledge bases (RAG patterns)

  • Autonomous and semi-autonomous agent workflows

  • Fine-tuning, prompt engineering, and optimization strategies

• Establish MLOps and LLMOps frameworks for model training, deployment, monitoring, evaluation, and lifecycle management.

• Define approaches for model observability, explainability (XAI), bias detection, and risk mitigation.

Technical Leadership & Collaboration

• Provide technical leadership and mentorship to solution architects, data engineers, da ta scientists, and AI engineers.

• Collaborate closely with platform, DevOps, and cloud engineering teams to enable automation-driven deployments.

• Review solution designs, conduct architecture assessments, and provide impact analysis and recommendations.

• Communicate complex technical concepts clearly to both technical and executive audiences.

Required Qualifications

• Bachelor s Degree in Engineering or a related technical discipline.

• 14+ years of hands-on experience in data architecture, analytics solutions, and/or cloud data platforms.

• 3+ years of hands-on experience delivering AI/ML and Generative AI solutions in production.

• 6+ years of experience designing and scaling enterprise data platforms on Google Cloud Platform, AWS, and Snowflake.

Preferred Qualifications

• Master s degree or Ph.D. preferred.

• Demonstrated success leading large-scale, cross-functional data and AI initiatives.

• Cloud platforms: Google Cloud Platform and AWS (multi-cloud experience strongly preferred)

• Data platforms: Snowflake, BigQuery, Data Lakes, Lakehouse architectures

• Programming & analytics: Python, SQL, PySpark

• AI/ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost

• GenAI/LLM frameworks, vector databases, and graph databases

• Data engineering tools: Spark, Kafka, Hadoop

• Containerization and orchestration: Docker, Kubernetes

• CI/CD and DevOps practices

• Strong understanding of data modeling, performance tuning, and cost optimization

• Strong architectural thinking and problem-solving skills

• Excellent communication and stakeholder management capabilities

• Ability to influence without authority and operate effectively in matrixed organizations

• Self-driven, organized, and able to manage multiple priorities