Engineering Manager – AWS to GCP Data Migration, AI/ML, GenAI

🔥 54 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

👮‍♀️ Software Engineering Manager

👻 Ghost score 12%

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Naveera Technology LLC

WebsiteLinkedInAll Job Openings

201 - 500 employees

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Naveera Technology LLC is a technology consulting and engineering firm that helps businesses become AI-first by building production-ready data engineering, machine learning, generative AI, application development, and IT infrastructure solutions. The company delivers end-to-end services—from data pipelines and cloud infrastructure to GenAI model development, MLOps, and custom web/mobile enterprise applications—focused on turning raw data into actionable intelligence and scalable systems. Naveera serves industries such as digital health, fintech, e-commerce, and enterprise clients through global delivery, extended teams, and a center-of-excellence model.

📋 Description

• Lead the end-to-end migration of enterprise data platforms from AWS to GCP • Assess AWS architecture, data pipelines, workloads, dependencies, and operational processes • Define target-state GCP architecture, migration roadmaps, phases, dependencies, risks, and rollback strategies • Lead architecture reviews and technical design discussions • Architect and implement scalable enterprise GCP Data Lake and Lakehouse platforms • Design data ingestion, transformation, consumption, batch, and real-time ETL/ELT frameworks • Architect streaming pipelines using Pub/Sub, Dataflow/Apache Beam, BigQuery, and Cloud Storage • Design enterprise data models, BigQuery partitioning, clustering, and analytics consumption models • Establish data governance, quality, lineage, metadata, ownership, security, IAM, encryption, and access policies • Lead Terraform infrastructure automation, CI/CD, testing, deployment, and environment standards • Optimize BigQuery, Dataflow, Spark, Cloud Storage, streaming workloads, performance, SLAs, and costs • Lead and mentor Data Engineers, Senior Data Engineers, and Technical Leads; set engineering standards and priorities • Track progress, risks, dependencies, milestones, coding, testing, security, and documentation practices • Serve as primary technical contact for US-based stakeholders and collaborate with Business, Product, Data Science, BI, DevOps, Security, and Analytics teams • Design and implement AI/ML and Generative AI solutions on GCP using Vertex AI and related services • Build production ML pipelines for preparation, training, validation, evaluation, deployment, monitoring, retraining, and lifecycle management • Develop RAG, enterprise search, document intelligence, AI assistant, summarization, semantic search, embeddings, vector search, and knowledge-management solutions • Implement MLOps, model versioning, experiment tracking, validation, testing, deployment approvals, rollback, and environment promotion • Monitor model performance, data drift, latency, reliability, inference cost, response quality, retrieval accuracy, hallucination, and prompt-injection risks • Ensure responsible AI, privacy, security, governance, access control, auditability, and human review • Partner with 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 • 5+ years of strong hands-on GCP Data Engineering experience • Strong hands-on experience with AWS Data Engineering and Data Architecture • 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 preferred • Google Cloud Professional Machine Learning Engineer certification preferred • Experience with Vertex AI Agent Builder, Vertex AI Search, Gemini models on Vertex AI, or enterprise Generative AI platforms preferred • Experience with dbt, Apache Airflow, Kafka, Apache Spark, Kubernetes, Cloud Run, and API-driven architectures preferred • Experience with Dataplex, Data Catalog, data lineage, metadata management, data governance, master data management, and data-quality frameworks preferred • Experience supporting enterprise or regulated environments with strong data privacy, security, compliance, audit, and governance requirements preferred • Required technical proficiency in AWS services including Amazon S3, AWS Glue, AWS Glue Data Quality, Amazon Redshift/Redshift Serverless, Amazon Athena, AWS Step Functions, AWS DMS, AWS Lake Formation, and IAM • Required technical proficiency in GCP services including BigQuery, Google Cloud Storage, Pub/Sub, Dataflow/Apache Beam, Cloud Composer/Airflow, Dataproc/Spark, Cloud Monitoring, Cloud Logging, Dataplex/Data Catalog • Required knowledge of ETL/ELT, CDC, batch and streaming data processing, event-driven architecture, data pipeline optimization, enterprise Data Lake/Lakehouse, Medallion Architecture, data modeling, dimensional modeling, multi-tenant data modeling, schema-on-read/schema-on-write, data lineage, metadata management, data governance, dbt, Apache Airflow, Terraform, Git/GitHub, Cloud Build, CI/CD, data quality frameworks, and OpenLineage (a plus)

🏖️ Benefits

• Flexible remote work environment • Exposure to global enterprise customers • Collaborative, innovation-driven engineering culture • Continuous learning and certification opportunities • Opportunity to lead large-scale AWS-to-GCP cloud transformation initiatives • Work on enterprise Data Lakehouse and analytics modernization projects