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Data Engineering Manager – GCP Cloud, AIML
Naveera Technology LLC. Lead a large-scale AWS-to-GCP data platform migration .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in architecting and implementing scalable data platforms on GCP, including Data Lake and Lakehouse architectures. Proficient in AI/ML solutioning, migration strategies, and establishing data governance standards.
Highest-signal resume keywords
GCP Data EngineeringAWS Data EngineeringAI/ML SolutioningBigQuery OptimizationData Lake Architecture
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringData ArchitectureCloud EngineeringAI/ML EngineeringETL/ELT Pipeline DevelopmentData ModelingData TransformationMLOps PracticesGenerative AI SolutionsReal-Time Data Processing
Soft Skills
CollaborationCommunicationStakeholder EngagementTechnical LeadershipProblem-Solving
Tools & Technologies
Google Cloud StorageBigQueryDataflowPub/SubCloud ComposerTerraformApache BeamVertex AICI/CD PipelinesIAM
Industry Keywords
Data LakeLakehouseCloud MigrationData GovernanceAI/ML Best Practices
Tech Stack
Tools & technologiesAirflowApacheAWSBigQueryCloudETLGoogle Cloud PlatformPySparkPythonSQLTerraform
About the role
Key responsibilities & impact- Lead a large-scale AWS-to-GCP data platform migration
- 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 batch, real-time, and event-driven ingestion, transformation, and consumption frameworks
- Establish standards for data modeling, partitioning, clustering, storage, metadata, lineage, and data access
- Analyze and optimize existing AWS data platforms before migration
- Map AWS services and workloads to equivalent or improved GCP services
- Prepare migration plans, modernization recommendations, technical roadmaps, and implementation plans
- Architect real-time pipelines using Pub/Sub, Dataflow, Apache Beam, BigQuery, and Cloud Storage
- Design and implement scalable batch and real-time ETL/ELT pipelines on GCP
- Develop transformation frameworks using Python, PySpark, SQL, Dataflow, Apache Beam, BigQuery, and dbt
- Build orchestration workflows using Cloud Composer and Apache Airflow
- Design enterprise data models and optimize BigQuery performance and cost
- Design and implement AI/ML and Generative AI solutions on GCP using Vertex AI and related GCP-native services
- Build production-grade machine learning pipelines for preparation, training, validation, 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 practices using Vertex AI Pipelines, Model Registry, Terraform, GitHub, Cloud Build, and CI/CD pipelines
- Establish model versioning, experiment tracking, validation, testing, deployment approval, rollback, and environment-promotion standards
- Monitor model performance, data drift, latency, reliability, inference cost, response quality, retrieval accuracy, and GenAI risks
- Ensure responsible AI, privacy, security, governance, access control, auditability, and human-review processes
- Partner with Data Science, Analytics, Product, BI, Security, and US-based stakeholders to deliver AI/ML and GenAI use cases
- Act as the primary technical point of contact for US-based stakeholders
- Translate business requirements into scalable data, cloud, AI/ML, and Generative AI solutions
- Present architecture decisions, migration strategies, roadmaps, implementation plans, risks, timelines, and trade-offs
- Communicate delivery status, dependencies, operational risks, and mitigation plans
- Collaborate with business teams to define operational, analytical, data-quality, platform, and AI/ML KPIs
Requirements
What you’ll need- 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
- 1–2 years of relevant experience in AIML Solutioning
- Strong hands-on experience with AWS Data Engineering and Data Architecture
- 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
Benefits
Comp & perks- Medical Insurance
- 10 company-paid holidays
- Supportive culture with one-of-a-kind growth opportunities
- Ability to work from home
- Peer-to-peer recognition program