Data Engineering Manager – GCP, AI/ML, GenAI

🔥 3 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 • Architect and implement scalable enterprise data platforms, Data Lake, and Lakehouse architectures on GCP • Design and implement AI/ML, Generative AI, RAG, and MLOps solutions using GCP and Vertex AI • Build production-grade machine learning pipelines covering preparation, training, validation, deployment, monitoring, retraining, and lifecycle management • Design ingestion, transformation, chunking, embedding, indexing, retrieval, batch, streaming, ETL, and ELT pipelines • Analyze AWS platforms and map AWS workloads and services to equivalent or improved GCP services • Define migration roadmaps, phases, dependencies, risks, rollback strategies, and target-state architectures • Design real-time event-driven pipelines using Pub/Sub, Dataflow/Apache Beam, BigQuery, and Cloud Storage • Define enterprise data models, BigQuery partitioning and clustering strategies, and analytics consumption models • Establish data governance, quality, lineage, metadata, security, privacy, access control, and responsible AI standards • Lead Terraform infrastructure automation, CI/CD, deployment, testing, and environment promotion across Dev, QA, UAT, and Production • Optimize performance, scalability, latency, throughput, reliability, and cloud costs; establish benchmarks and SLAs • Lead and mentor Data Engineers, Senior Data Engineers, and Technical Leads; conduct architecture and code reviews • Track engineering progress, risks, dependencies, and delivery milestones • Serve as primary technical contact for US-based stakeholders and collaborate with Business, Product, Data Science, BI, DevOps, Security, and Analytics teams • Translate business requirements into technical solutions and present architecture decisions, migration strategies, roadmaps, risks, and trade-offs

🎯 Requirements

• 15+ years of experience in GCP Data Engineering, Data Architecture, Cloud Engineering, AI/ML Engineering, or related technology leadership roles • 5+ years of strong hands-on GCP Data Engineering experience • 3+ years of strong hands-on AI/ML and GenAI 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 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 skills include Python, PyTorch, TensorFlow, Scikit-learn, NLP, deep learning, ML algorithms, GenAI, LLMs, GPT, Gemini, Claude, Llama, prompt engineering, fine-tuning, RAG, embeddings, vector databases, semantic search, hybrid search, reranking, LangChain, LlamaIndex, LangGraph, Hugging Face, Transformers, AI agents, agentic workflows, tool/function calling, multi-agent systems, MCP, MLflow, Kubeflow, model registry, model deployment, monitoring, CI/CD, Vertex AI, Vertex AI Studio, Vertex AI Pipelines, Model Garden, Vector Search, FastAPI, Flask, REST APIs, SQL, Docker, Kubernetes, GCP, AWS, Azure, BigQuery, Dataflow, Spark, Databricks, data lakes, advanced Python, expert SQL, PySpark, Apache Spark, ETL, ELT, CDC, batch and streaming, event-driven architecture, data pipeline development and optimization, enterprise Data Lake/Lakehouse, Medallion Architecture, data warehousing, data modeling, dimensional modeling, multi-tenant modeling, schema-on-read/schema-on-write, dbt, Apache Airflow/Cloud Composer, Dataproc, Dataflow/Apache Beam, AWS Glue, Redshift, EMR, Lambda, Kinesis, Athena, CloudWatch, GCS, AWS S3, Pub/Sub, Dataplex, Data Catalog, Terraform, Git/GitHub, Cloud Build, CI/CD, Infrastructure as Code, data lineage, metadata management, data quality, monitoring, and OpenLineage

🏖️ Benefits

• Flexible remote work • Exposure to global customers • Collaborative, innovation-driven culture • Continuous learning and certification • Opportunity to lead transformative AI/ML and GCP innovations as Head of Engineering