[Hiring] Data Engineering Manager @Naveera Technology LLC

Role Description

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.

Key Responsibilities

• 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.
• 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.
• 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.
• 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.

Qualifications

• 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 & Gen AI 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.
• Google Cloud Professional Machine Learning Engineer certification.
• Experience with Vertex AI Agent Builder, Vertex AI Search, Gemini models on Vertex AI, or enterprise Generative AI platforms.
• Experience with dbt, Apache Airflow, Kafka, Apache Spark, Kubernetes, Cloud Run, and API-driven architectures.
• Experience with Dataplex, Data Catalog, data lineage, metadata management, data governance, master data management, and data-quality frameworks.
• Experience supporting enterprise or regulated environments with strong data privacy, security, compliance, audit, and governance requirements.

Benefits

• Lead AWS-to-GCP cloud transformation & AIML GenAI initiatives.
• Work on Data Lakehouse and analytics modernization.
• Flexible remote work.
• Exposure to global customers.
• Collaborative, innovation-driven culture.
• Continuous learning and certification.
• Lead transformative AI/ML & GCP innovations as Head of Engineering at Naveera Tech.
• Join a global team to revolutionize data into business value.
• Remote role, USA-based.
• Apply today!