Google Cloud Platform Data Architect

Hi, Hope you are doing well. Position: AI Data Solution ArchitectLocation: Atlanta, GA (Hybrid) Job Description: Strategic & Architectural LeadershipDefine 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 DesignLead 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 ArchitectureDesign 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 & CollaborationProvide technical leadership and mentorship to solution architects, data engineers, data 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 QualificationsBachelor 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 QualificationsMaster 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