Engineering Manager – AWS to GCP Data Migration, AI/ML & GenAI
Naveera Technology LLC
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Job summary
Naveera Technology LLC in India seeks a seasoned Engineering Manager with deep AWS and GCP data engineering expertise to lead a large-scale AWS-to-GCP migration. You will shape target architecture, drive delivery, and mentor a multidisciplinary team, coordinating with data science, product, analytics, and security stakeholders.
The role also spans building AI/ML, Generative AI, and MLOps capabilities on GCP, using Vertex AI and BigQuery, with a focus on secure, scalable, and governed data
Qualifications
- 15+ years in data engineering, architecture, or cloud leadership.
- Strong hands-on AWS and GCP data platform experience.
- Proven track record leading large migrations.
- Excellent stakeholder and team leadership.
- Experience with Vertex AI and Generative AI is a plus.
Responsibilities
- Lead end-to-end AWS-to-GCP data-migration for large platforms.
- Architect scalable GCP data platforms and data lakehouse layers.
- Oversee AI/ML, Generative AI, and MLOps on GCP.
- Manage cross-functional teams and stakeholder communications.
- Define migration roadmaps, risks, and delivery milestones.
Skills
Education
Tools
Job description
We are looking for an experienced Engineering Manager with strong hands‑on expertise in AWS and GCP Data Engineering to lead a large‑scale AWS-to-GCP data platform migration.
The ideal candidate will have strong experience designing enterprise data platforms on AWS and migrating them to Google Cloud Platform (GCP). The role requires a combination of technical architecture, hands‑on engineering, migration leadership, team management, stakeholder management, and delivery ownership.
In addition to AWS-to-GCP data migration, this role will lead the design and delivery of AI/ML, Generative AI, and MLOps capabilities on GCP. The candidate will work with Data Science, Product, Business, Analytics, BI, and Engineering teams to build secure, scalable, governed, and production‑ready data and AI solutions using GCP‑native services.
All target‑state data engineering, AI/ML, Generative AI, and MLOps architecture and implementation experience must be on GCP.
- 1. AWS to GCP Migration Leadership
Lead the end‑to‑end migration of enterprise data platforms from AWS to GCP.
Assess existing AWS architecture, data pipelines, workloads, dependencies, data models, and operational processes.
Define target‑state GCP 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 GCP-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.
- 2. GCP Data Platform Architecture
Architect and implement scalable enterprise data platforms on GCP.
Design Data Lake and Lakehouse architectures using Google Cloud Storage and BigQuery.
Define Bronze, Silver, and Gold/Atomic data layers.
Design scalable batch, real‑time, and event‑driven ingestion, transformation, and data‑consumption frameworks.
Establish standards for data modeling, partitioning, clustering, storage, metadata, lineage, and data access.
Design multi‑tenant and multi‑location data architectures.
Define schema‑on‑read and schema‑on‑write strategies.
Build scalable data platforms that support analytics, BI, real‑time reporting, machine learning, and Generative AI use cases.
- 3. AWS Data Platform Expertise
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 GCP services for AWS data workloads.
Prepare technical mapping, modernization recommendations, migration plans, and implementation roadmaps between AWS and GCP services.
- 4. GCP Streaming & Real‑Time Data Engineering
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‑injection patterns.
Implement monitoring, logging, alerting, and operational support processes for real‑time workloads.
- 5. ETL/ELT & Data Processing
Design and implement scalable batch and real‑time ETL/ELT pipelines on GCP.
Migrate AWS Glue‑based pipelines to appropriate GCP‑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.
- 6. Data Modeling & BigQuery
Design enterprise data models for analytics, reporting, operational intelligence, and AI/ML workloads.
Define dimensional, normalized, denormalized, and multi‑tenant data models.
Design BigQuery partitioning, clustering, storage, and query‑optimization strategies.
Optimize BigQuery SQL and query execution for performance and cost.
Design data models that support real‑time and batch workloads.
Work closely with BI and Analytics teams to build scalable self‑service consumption models.
- 7. AI/ML, Generative AI & MLOps
Design and implement AI/ML and Generative AI solutions on GCP using Vertex AI, BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Run, and related GCP‑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.
- 8. Data Governance, Security & Quality
Establish data governance, data‑quality, metadata, lineage, and ownership standards across the GCP 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 GCP 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.
- 9. DevOps, Infrastructure & Automation
Lead infrastructure automation using Terraform.
Build repeatable, scalable, secure, and compliant GCP 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 GCP data, AI/ML, and GenAI workloads.
- 10. Performance & Cost Optimization
Lead performance‑optimization initiatives across GCP 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 GCP 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.
- 11. Engineering Management & Team Leadership
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 GCP, data architecture, modern data engineering, AI/ML, Generative AI, and MLOps practices.
- 12. Stakeholder & Client Management
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 GCP Data Engineering experience.
- Proven experience delivering AWS-to-GCP migration projects.
- Strong experience designing enterprise Data Lake and Lakehouse platforms on GCP.
- 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 GCP.
- Strong knowledge of AWS and GCP service mapping, migration patterns, modernization strategies, and cloud architecture best practices.
- Experience designing, building, and deploying AI/ML solutions on GCP 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.
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