Lead the end-to-end migration of enterprise data platforms from AWS to Google Cloud Platform.
Assess existing AWS architecture, data pipelines, workloads, dependencies, data models, and operational processes.
Define target-state Google Cloud Platform architecture, migration roadmap, technical dependencies, risks, rollback strategies, and delivery milestones.
Develop migration strategies for:
Amazon S3 to Google Cloud Storage
Amazon Redshift to BigQuery
AWS Glue to Dataflow, Dataproc, or BigQuery
AWS Step Functions to Cloud Composer or Workflows
AWS DMS to Google Cloud Platform-native CDC solutions
Amazon Athena to BigQuery
Identify opportunities to modernize AWS workloads rather than performing a simple lift-and-shift migration.
Lead architecture reviews, technical design discussions, migration planning, and implementation governance.
Analyze and optimize existing AWS data platforms before migration.
Work with Amazon S3, AWS Glue, AWS Glue Data Quality, Amazon Redshift, Redshift Serverless, Amazon Athena, AWS Step Functions, AWS DMS, and AWS Lake Formation.
Understand existing AWS ETL/ELT pipelines, data models, workloads, security controls, and platform dependencies.
Identify equivalent or improved Google Cloud Platform services for AWS data workloads.
Prepare technical mapping, modernization recommendations, migration plans, and implementation roadmaps between AWS and Google Cloud Platform services.
Architect real-time data pipelines using Google Pub/Sub, Dataflow, Apache Beam, BigQuery, and Cloud Storage.
Design high-volume event ingestion, enrichment, transformation, and delivery pipelines.
Implement event-driven architectures with appropriate delivery guarantees.
Optimize streaming pipelines for latency, throughput, scalability, reliability, and cost.
Design BigQuery streaming-ingestion patterns.
Implement monitoring, logging, alerting, and operational support processes for real-time workloads.
Design and implement scalable batch and real-time ETL/ELT pipelines on Google Cloud Platform.
Migrate AWS Glue-based pipelines to appropriate Google Cloud Platform-native services.
Develop transformation frameworks using Python, PySpark, SQL, Dataflow, Apache Beam, BigQuery, and dbt.
Design CDC pipelines and real-time ingestion patterns.
Build orchestration workflows using Cloud Composer and Apache Airflow.
Optimize data-processing jobs, Spark workloads, pipeline execution, and query performance.
Establish coding, testing, documentation, deployment, and operational standards for data engineering workloads.
Design and implement AI/ML and Generative AI solutions on Google Cloud Platform using Vertex AI, BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Run, and related Google Cloud Platform-native services.
Build production-grade machine learning pipelines for data preparation, model training, validation, evaluation, deployment, monitoring, retraining, and lifecycle management.
Develop Generative AI and Retrieval-Augmented Generation (RAG) solutions, including enterprise search, document intelligence, AI assistants, summarization, semantic search, embeddings, vector search, and knowledge-management applications.
Design scalable ingestion, transformation, chunking, embedding, indexing, and retrieval pipelines for structured and unstructured enterprise data.
Implement MLOps practices using Vertex AI Pipelines, Model Registry, model endpoints, Terraform, GitHub, Cloud Build, and CI/CD pipelines.
Establish standards for model versioning, experiment tracking, data and feature validation, automated testing, deployment approvals, rollback, and environment promotion.
Implement monitoring for model performance, data drift, latency, reliability, inference cost, response quality, retrieval accuracy, and GenAI risks such as hallucination and prompt injection.
Ensure responsible AI, data privacy, security, governance, access control, auditability, and human-review processes are incorporated into AI/ML and GenAI solutions.
Partner with Data Science, Analytics, Product, BI, Security, and US-based stakeholders to identify, prioritize, and deliver high-value AI/ML and GenAI use cases.
Establish data governance, data-quality, metadata, lineage, and ownership standards across the Google Cloud Platform data platform.
Implement automated data-quality checks, validation frameworks, reconciliation processes, and monitoring.
Establish governance and quality standards for AI/ML datasets, features, models, prompts, embeddings, vector stores, and Generative AI applications.
Ensure appropriate security controls across all Google Cloud Platform data layers, including IAM, least-privilege access, encryption, service accounts, network security, secrets management, and data access policies.
Ensure secure handling of confidential, sensitive, regulated, and personally identifiable information used in data, AI/ML, and GenAI workloads.
Partner with governance, security, legal, and compliance teams to meet enterprise and regulatory requirements.
Experience with Dataplex, Data Catalog, data lineage, and responsible AI governance is preferred.
Lead infrastructure automation using Terraform.
Build repeatable, scalable, secure, and compliant Google Cloud Platform infrastructure deployments.
Implement CI/CD pipelines for data engineering, AI/ML models, Vertex AI pipelines, Generative AI applications, and infrastructure deployments.
Work with Terraform, Git, GitHub, Cloud Build, Artifact Registry, and CI/CD pipelines.
Automate data-pipeline deployment, testing, validation, security scanning, approvals, and rollback mechanisms.
Establish Development, QA, UAT, and Production deployment standards for Google Cloud Platform data, AI/ML, and GenAI workloads.
Lead performance-optimization initiatives across Google Cloud Platform data workloads.
Optimize BigQuery query performance, partitioning, clustering, Dataflow pipelines, Spark workloads, Cloud Storage, and streaming workloads.
Analyze AWS workloads and define the most cost-effective target-state Google Cloud Platform architecture.
Develop cloud FinOps, capacity-planning, budget-monitoring, and cost-optimization strategies.
Optimize AI/ML and Generative AI workloads for training cost, inference cost, latency, throughput, model selection, storage, and compute utilization.
Establish performance benchmarks, SLAs, SLOs, and cost controls for critical data and AI services.
Lead and mentor a team of Data Engineers, Senior Data Engineers, ML Engineers, Technical Leads, and other engineering resources.
Provide technical direction and establish engineering standards.
Conduct architecture reviews, code reviews, design reviews, and technical planning sessions.
Define technical roadmaps, engineering priorities, delivery plans, and modernization strategies.
Break complex migration and AI/ML requirements into actionable deliverables.
Track engineering progress, risks, dependencies, delivery milestones, and quality metrics.
Promote best practices around coding, testing, CI/CD, data quality, security, governance, documentation, and operational excellence.
Mentor engineers on Google Cloud Platform, data architecture, modern data engineering, AI/ML, Generative AI, and MLOps practices.
Act as the primary technical point of contact for US-based stakeholders.
Work closely with Business, Product, Data Science, BI, Analytics, DevOps, Security, and Architecture teams.
Translate business requirements into scalable data, cloud, AI/ML, and Generative AI solutions.
Present architecture decisions, migration strategies, technical roadmaps, implementation plans, risks, timelines, and trade-offs.
Communicate technical dependencies, delivery status, operational risks, and mitigation plans clearly to technical and non-technical stakeholders.
Collaborate with business teams to define operational, analytical, data-quality, platform, and AI/ML KPIs.
15+ years of experience in Data Engineering, Data Architecture, Cloud Engineering, AI/ML Engineering, or related technology leadership roles.
Strong hands-on experience with AWS Data Engineering and Data Architecture.
5+ years of strong hands-on Google Cloud Platform Data Engineering experience.
Proven experience delivering AWS-to-Google Cloud Platform migration projects.
Strong experience designing enterprise Data Lake and Lakehouse platforms on Google Cloud Platform.
Strong hands-on experience with BigQuery, Google Cloud Storage, Dataflow, Pub/Sub, Cloud Composer, Dataproc, IAM, and Terraform.
Experience migrating AWS data workloads, pipelines, and platforms to Google Cloud Platform.
Strong knowledge of AWS and Google Cloud Platform service mapping, migration patterns, modernization strategies, and cloud architecture best practices.
Experience designing, building, and deploying AI/ML solutions on Google Cloud Platform using Vertex AI.
Hands-on experience with Generative AI, LLM-based applications, RAG architectures, embeddings, vector search, prompt engineering, and enterprise AI assistants.
Strong understanding of MLOps, including model training, model registry, CI/CD/CT, model deployment, monitoring, retraining, governance, and rollback strategies.
Experience implementing secure and responsible AI solutions, including data privacy, model evaluation, access controls, auditability, and governance.
Expert-level SQL and strong Python and PySpark skills.
Strong data modeling, data warehousing, batch processing, and real-time data engineering experience.
Experience with Terraform, Git, GitHub, Cloud Build, CI/CD pipelines, and infrastructure automation.
Experience managing and mentoring data engineering and cross-functional technical teams.
Strong communication skills with experience working with US-based stakeholders.
Google Cloud Professional Data Engineer certification.
Google Cloud Professional Machine Learning Engineer certification.
Experience with Vertex AI Agent Builder, Vertex AI Search, Gemini models on Vertex AI, or enterprise Generative AI platforms.
Experience with dbt, Apache Airflow, Kafka, Apache Spark, Kubernetes, Cloud Run, and API-driven architectures.
Experience with Dataplex, Data Catalog, data lineage, metadata management, data governance, master data management, and data-quality frameworks.
Experience supporting enterprise or regulated environments with strong data privacy, security, compliance, audit, and governance requirements.
