Engineering Manager (AWS to GCP Data Migration, AI/ML & GenAI) @ Naveera Tech, USA - Remote Work

Position Overview

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 and stakeholder management. The candidate should have strong hands-on experience with AWS services such as S3, Glue, Redshift, Athena, Step Functions and AWS DMS, along with strong GCP expertise across BigQuery, Dataflow, Pub/Sub, Cloud Storage and Cloud Composer. The Engineering Manager will work closely with US-based stakeholders, architects, engineers, DevOps teams, Data Science and BI teams to define the migration strategy and ensure successful execution.

Key Responsibilities

• 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 and operational processes. Define the target-state GCP architecture and migration roadmap. Develop migration strategies for: Amazon S3 → Google Cloud Storage, Amazon Redshift → BigQuery, AWS Glue → Dataflow / Dataproc / BigQuery, AWS Step Functions → Cloud Composer / Workflows, AWS DMS → GCP-native CDC solutions, Amazon Athena → BigQuery. Identify opportunities to modernize AWS workloads rather than performing a simple lift-and-shift migration. Define migration phases, technical dependencies, risks and rollback strategies. Lead architecture reviews and technical design discussions.
• GCP Data Platform Architecture: Architect and implement scalable enterprise data platforms on GCP. Design Data Lake and Lakehouse architectures using GCS and BigQuery. Define Bronze, Silver and Gold/Atomic data layers. Design scalable data ingestion, transformation and consumption frameworks. Establish standards for data modeling, partitioning, clustering and storage. Design multi-tenant and multi-location data architectures. Define schema-on-read and schema-on-write strategies.
• 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, AWS Lake Formation. Understand existing AWS ETL/ELT pipelines, data models, workloads and dependencies. Identify equivalent or improved GCP services for each AWS workload. Prepare technical mapping and migration plans between AWS and GCP services.
• GCP Streaming & Real-Time Data Engineering: Architect real-time data pipelines using: Google Pub/Sub, Dataflow / Apache Beam, BigQuery, Cloud Storage. Design high-volume event ingestion, enrichment and transformation pipelines. Implement event-driven architectures and appropriate delivery guarantees. Optimize streaming pipelines for latency, throughput and scalability. Design BigQuery streaming ingestion patterns. Implement monitoring, logging and alerting for real-time workloads.
• ETL / ELT & Data Processing: Design and implement scalable batch and real-time ETL/ELT pipelines. Migrate AWS Glue-based pipelines to appropriate GCP services. Develop transformation frameworks using: Python, PySpark, SQL, Dataflow / Apache Beam, BigQuery, dbt. Design CDC pipelines and real-time ingestion patterns. Build orchestration workflows using Cloud Composer / Airflow. Optimize data processing jobs and query performance.
• Data Modeling & BigQuery: Design enterprise data models for analytics and reporting. Define dimensional, normalized and denormalized data models. Develop multi-tenant data structures. Design BigQuery partitioning and clustering strategies. Optimize BigQuery SQL and query execution. Design data models supporting both real-time and batch workloads. Work closely with BI and Analytics teams to create scalable consumption models.
• Data Governance, Security & Quality: Establish data governance and data quality standards across the GCP platform. Implement automated data quality checks and validation frameworks. Establish data lineage, metadata and ownership standards. Ensure appropriate security controls across all GCP data layers. Implement: IAM, Least-privilege access, Encryption, Service accounts, Network security, Data access policies. Work with governance and security teams to ensure compliance requirements are met. Experience with Dataplex, Data Catalog and data lineage is preferred.
• DevOps, Infrastructure & Automation: Lead infrastructure automation using Terraform. Build repeatable and secure GCP infrastructure deployments. Implement CI/CD pipelines for data engineering workloads. Work with: Terraform, Git, GitHub, Cloud Build, CI/CD pipelines. Automate data pipeline deployment, testing and infrastructure provisioning. Establish Dev, QA, UAT and Production deployment standards.
• Performance & Cost Optimization: Lead performance optimization initiatives across GCP data workloads. Optimize: BigQuery query performance, Partitioning and clustering, Dataflow pipelines, Spark workloads, Cloud Storage, Streaming workloads. Analyze AWS workloads and determine the most cost-effective GCP architecture. Develop cloud FinOps and cost optimization strategies. Establish performance benchmarks and SLAs for critical workloads.
• Engineering Management & Team Leadership: Lead and mentor a team of Data Engineers, Senior Data Engineers and Technical Leads. Provide technical direction and establish engineering standards. Conduct architecture and code reviews. Define technical roadmaps and engineering priorities. Break complex migration requirements into actionable deliverables. Track engineering progress, risks, dependencies and delivery milestones. Promote best practices around coding, testing, CI/CD, security and documentation. Mentor engineers on GCP, data architecture and modern data engineering practices.
• Stakeholder & Client Management: Act as the primary technical point of contact for US-based stakeholders. Work closely with Business, Product, Data Science, BI and DevOps teams. Translate business requirements into scalable technical solutions. Present architecture decisions, migration strategies and technical roadmaps. Communicate technical risks, dependencies, timelines and trade-offs. Collaborate with business teams to define operational and analytical KPIs.
• 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.

Requirements

• 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.

Preferred Qualifications

• Google Cloud Professional Data Engineer certification.