{"id":4085842,"url":"https://alion.io/job/naveera-technology-llc-data-engineering-manager-gcp-cloud-aiml","title":"Data Engineering Manager – GCP Cloud & AIML","company":{"id":684157,"name":"Naveera Technology LLC","domain":"naveeratech.com","url":"https://alion.io/company/naveeratech","size_band":"11-50","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Join","truth_index":null},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":15,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"dbt","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"GitHub","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Hallucination","optional":false},{"name":"IAM","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-11-11T13:03:47Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-11T18:52:22Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Greetings of the day!!\nI am Arumugam Veera, reaching out to you regarding an exciting career opportunity with Naveera Technology LLC. I would be happy to connect and discuss the opportunity further. You can also connect with me on LinkedIn:https://www.linkedin.com/in/arumugamv/\nAbout Naveera Technology LLC\nNaveera Technology LLC is a trusted global engineering partner delivering Data Engineering, Generative AI, Application Development, and IT Infrastructure solutions. With over 15 years of experience in IT services and consulting, we help organizations transform raw data into actionable business value.\nWith a team of 100+ employees and successful delivery of 3+ global projects, Naveera serves clients across multiple industries and geographies through agile delivery models and proven engineering practices.\nFrom Digital Health and Financial Services to E-Commerce and Technology, we support a diverse client base and back every engagement with proven frameworks, low-attrition teams, and scalable global delivery capabilities. At Naveera, we empower organizations to turn challenges into opportunities, data into insights, and innovative ideas into enterprise-grade platforms.\nSpecialties\nData Engineering & Modern Data Stack, Generative AI Solutions & Model Deployment, Application Development (Web, Mobile & Enterprise), Artificial Intelligence (Predictive, Conversational, Computer Vision), IT Infrastructure Services (Cloud & On-Prem), Security, DR, Cloud Transformation & Microservices, DevOps, API & Systems Integration, Extended Technology Teams & Dedicated Delivery, Real-Time Streaming & Analytics, and BI & Data Warehousing\nJob Title: Engineering Manager - GCP & AIML\n\nExperience: 15+ Years\n\nJob Location: USA (Remote)\n\nProfile location: Anywhere in USA\n\nPrimary Focus: GCP & AI/ML & GenAI | Data Engineering | Data Architecture | Engineering\n\nInterview Levels: 3 Levels of Interview from Naveera + 1 Level of client discussion\n\nPls find the below benefit details for the above role.\nMedical Insurance\n\n10 Company Paid holidays\n\nSupportive culture with one-of-a-kind growth opportunities\n\nAbility to work from home\n\nPeer-to-peer recognition program\n\nTotal experience must be 15+ years, and the primary focus must be below:\n5+ years of strong hands-on relevant GCP Data Engineering experience is mandate\n\n1-2 years of relevant experience in AIML Solutioning\n\nPosition Overview\nWe 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.\nThe 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.\nIn 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.\nAll target-state data engineering, AI/ML, Generative AI, and MLOps architecture and implementation experience must be on GCP.\nKey Responsibilities\n1. GCP Data Platform Architecture\nArchitect and implement scalable enterprise data platforms on GCP.\n\nDesign Data Lake and Lakehouse architectures using Google Cloud Storage and BigQuery.\n\nDefine Bronze, Silver, and Gold/Atomic data layers.\n\nDesign scalable batch, real-time, and event-driven ingestion, transformation, and data-consumption frameworks.\n\nEstablish standards for data modeling, partitioning, clustering, storage, metadata, lineage, and data access.\n\nDesign multi-tenant and multi-location data architectures.\n\nDefine schema-on-read and schema-on-write strategies.\n\nBuild scalable data platforms that support analytics, BI, real-time reporting, machine learning, and Generative AI use cases.\n\n2. AWS Data Platform Expertise\nAnalyze and optimize existing AWS data platforms before migration.\n\nWork with Amazon S3, AWS Glue, AWS Glue Data Quality, Amazon Redshift, Redshift Serverless, Amazon Athena, AWS Step Functions, AWS DMS, and AWS Lake Formation.\n\nUnderstand existing AWS ETL/ELT pipelines, data models, workloads, security controls, and platform dependencies.\n\nIdentify equivalent or improved GCP services for AWS data workloads.\n\nPrepare technical mapping, modernization recommendations, migration plans, and implementation roadmaps between AWS and GCP services.\n\n3. GCP Streaming & Real-Time Data Engineering\nArchitect real-time data pipelines using Google Pub/Sub, Dataflow, Apache Beam, BigQuery, and Cloud Storage.\n\nDesign high-volume event ingestion, enrichment, transformation, and delivery pipelines.\n\nImplement event-driven architectures with appropriate delivery guarantees.\n\nOptimize streaming pipelines for latency, throughput, scalability, reliability, and cost.\n\nDesign BigQuery streaming-ingestion patterns.\n\nImplement monitoring, logging, alerting, and operational support processes for real-time workloads.\n\n4. ETL/ELT & Data Processing\nDesign and implement scalable batch and real-time ETL/ELT pipelines on GCP.\n\nMigrate AWS Glue-based pipelines to appropriate GCP-native services.\n\nDevelop transformation frameworks using Python, PySpark, SQL, Dataflow, Apache Beam, BigQuery, and dbt.\n\nDesign CDC pipelines and real-time ingestion patterns.\n\nBuild orchestration workflows using Cloud Composer and Apache Airflow.\n\nOptimize data-processing jobs, Spark workloads, pipeline execution, and query performance.\n\nEstablish coding, testing, documentation, deployment, and operational standards for data engineering workloads.\n\n5. Data Modeling & BigQuery\nDesign enterprise data models for analytics, reporting, operational intelligence, and AI/ML workloads.\n\nDefine dimensional, normalized, denormalized, and multi-tenant data models.\n\nDesign BigQuery partitioning, clustering, storage, and query-optimization strategies.\n\nOptimize BigQuery SQL and query execution for performance and cost.\n\nDesign data models that support real-time and batch workloads.\n\nWork closely with BI and Analytics teams to build scalable self-service consumption models.\n\n6. AI/ML, Generative AI & MLOps\nDesign 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.\n\nBuild production-grade machine learning pipelines for data preparation, model training, validation, evaluation, deployment, monitoring, retraining, and lifecycle management.\n\nDevelop 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.\n\nDesign scalable ingestion, transformation, chunking, embedding, indexing, and retrieval pipelines for structured and unstructured enterprise data.\n\nImplement MLOps practices using Vertex AI Pipelines, Model Registry, model endpoints, Terraform, GitHub, Cloud Build, and CI/CD pipelines.\n\nEstablish standards for model versioning, experiment tracking, data and feature validation, automated testing, deployment approvals, rollback, and environment promotion.\n\nImplement monitoring for model performance, data drift, latency, reliability, inference cost, response quality, retrieval accuracy, and GenAI risks such as hallucination and prompt injection.\n\nEnsure responsible AI, data privacy, security, governance, access control, auditability, and human-review processes are incorporated into AI/ML and GenAI solutions.\n\nPartner with Data Science, Analytics, Product, BI, Security, and US-based stakeholders to identify, prioritize, and deliver high-value AI/ML and GenAI use cases.\n\n7. Stakeholder & Client Management\nAct as the primary technical point of contact for US-based stakeholders.\n\nWork closely with Business, Product, Data Science, BI, Analytics, DevOps, Security, and Architecture teams.\n\nTranslate business requirements into scalable data, cloud, AI/ML, and Generative AI solutions.\n\nPresent architecture decisions, migration strategies, technical roadmaps, implementation plans, risks, timelines, and trade-offs.\n\nCommunicate technical dependencies, delivery status, operational risks, and mitigation plans clearly to technical and non-technical stakeholders.\n\nCollaborate with business teams to define operational, analytical, data-quality, platform, and AI/ML KPIs.\n\nRequired Qualifications\n15+ years of experience in Data Engineering, Data Architecture, Cloud Engineering, AI/ML Engineering, or related technology leadership roles.\n\nStrong hands-on experience with AWS Data Engineering and Data Architecture.\n\n5+ years of strong hands-on GCP Data Engineering experience.\n\n1-2 yrs in AIML Solutioning.\n\nStrong experience designing enterprise Data Lake and Lakehouse platforms on GCP.\n\nStrong hands-on experience with BigQuery, Google Cloud Storage, Dataflow, Pub/Sub, Cloud Composer, Dataproc, IAM, and Terraform.\n\nExperience migrating AWS data workloads, pipelines, and platforms to GCP.\n\nStrong knowledge of AWS and GCP service mapping, migration patterns, modernization strategies, and cloud architecture best practices.\n\nExperience designing, building, and deploying AI/ML solutions on GCP using Vertex AI.\n\nHands-on experience with Generative AI, LLM-based applications, RAG architectures, embeddings, vector search, prompt engineering, and enterprise AI 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