About Naveera Technology LLC
Naveera 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.
With 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.
From 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.
Specialties
Data 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
Job Title: Engineering Manager – AWS to GCP Migration
Experience: 15+ Years
Location: Remote (USA)
Primary Focus: AWS → GCP Migration | Data Engineering | Data Architecture | Engineering Leadership
Position Overview
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.
The candidate should have strong hands‑on experience with AWS services such as S3, Glue, Redshift, Athena, Step Functions and AWS DMS, along with strong GCP expertise across BigQuery, Dataflow, Pub/Sub, Cloud Storage and Cloud Composer.
The Engineering Manager will work closely with US‑based stakeholders, architects, engineers, DevOps teams, Data Science and BI teams to define the migration strategy and ensure successful execution.
Key Responsibilities
1. 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:
- AWS Step Functions → Cloud Composer / Workflows
Identify opportunities to modernise 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.
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 modelling, partitioning, clustering and storage.
Design multi‑tenant and multi‑location data architectures.
Define schema‑on‑read and schema‑on‑write strategies.
3. AWS Data Platform Expertise
Analyze and optimise existing AWS data platforms before migration.
Work with:
- Amazon S3
- AWS Glue
- AWS Glue Data Quality
- 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.
- Google Pub/Sub
- Dataflow / Apache Beam
- BigQuery
Design high‑volume event ingestion, enrichment and transformation pipelines.
Implement event‑driven architectures and appropriate delivery guarantees.
Optimise streaming pipelines for latency, throughput and scalability.
Implement monitoring, logging and alerting for real‑time workloads.
5. 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.
Optimise data processing jobs and query performance.
6. Data Modelling & BigQuery
Design enterprise data models for analytics and reporting.
Define dimensional, normalised and denormalised data models.
Design BigQuery partitioning and clustering strategies.
Optimise 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.
7. 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
- Encryption
- Service accounts
- Network security
Work with governance and security teams to ensure compliance requirements are met.
Experience with Dataplex, Data Catalog and data lineage is preferred.
8. DevOps, Infrastructure & Automation
Build repeatable and secure GCP infrastructure deployments.
Work with:
- Terraform
- Git
- GitHub
Automate data pipeline deployment, testing and infrastructure provisioning.
Establish Dev, QA, UAT and Production deployment standards.
9. Performance & Cost Optimisation
Lead performance optimisation initiatives across GCP data workloads.
Optimise:
- BigQuery query performance
- Partitioning and clustering
- Dataflow pipelines
Analyse AWS workloads and determine the most cost‑effective GCP architecture.
Develop cloud FinOps and cost optimisation strategies.
Establish performance benchmarks and SLAs for critical workloads.
10. 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.
11. 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.
Tasks
- 15+ years of experience in Data Engineering, Data Architecture or Cloud Engineering.
- Strong hands‑on experience with AWS Data Engineering and Architecture.
- 5+ years of hands‑on GCP Data Engineering experience.
- Proven experience working on AWS‑to‑GCP migration projects.
- Strong experience designing enterprise Data Lake / Lakehouse platforms.
- Experience migrating AWS data workloads to GCP.
- Strong knowledge of AWS and GCP service mapping and cloud migration patterns.
- Expert‑level SQL and strong Python/PySpark skills.
- Strong data modelling and data warehousing experience.
- Experience with Terraform and CI/CD.
- Experience managing and mentoring data engineering teams.
- Strong communication skills with experience working with US‑based stakeholders.
Ability to assess AWS workloads, define the GCP target architecture and lead the migration from strategy through production implementation.
Solution Architecture
Strong ability to translate complex business and technical requirements into scalable, secure and cost‑effective GCP architectures.
Engineering Leadership
Ability to manage, mentor and technically guide Data Engineering teams while remaining hands‑on with critical architecture and implementation decisions.
Data Architecture
Strong expertise in Data Lakehouse, Data Warehouse, Streaming, Data Modelling and modern data engineering patterns.
Performance & Cost Engineering
Ability to optimise BigQuery, Dataflow and Spark workloads while implementing cloud cost optimisation strategies.
Data Governance & Security
Strong understanding of data quality, lineage, metadata, IAM, encryption, access control and enterprise governance.
Stakeholder Management
Excellent communication skills with the ability to work directly with US‑based business and technical stakeholders.
Requirements
Required Technical Skills & Cloud Platforms
AWS – Strong Existing Platform Experience
- Amazon S3
- AWS Glue
- AWS Glue Data Quality
- Amazon Redshift / Redshift Serverless
- Amazon Athena
- AWS Step Functions
- AWS DMS
- AWS Lake Formation
- IAM
GCP – Target Platform
- BigQuery
- Google Cloud Storage
- Pub/Sub
- Dataflow / Apache Beam
- Cloud Composer / Airflow
- Dataproc / Spark
- Cloud Monitoring
- Cloud Logging
- Dataplex / Data Catalog
Data Engineering
- Advanced Python
- Expert‑level SQL
- Strong PySpark / Apache Spark
- ETL/ELT
- CDC
- Batch and streaming data processing
- Event‑driven architecture
- Data pipeline optimisation
Data Architecture
- Enterprise Data Lake / Lakehouse
- Medallion Architecture
- Data Modelling
- Dimensional Modelling
- Multi‑tenant Data Modelling
- Schema‑on‑Read / Schema‑on‑Write
- Data Lineage
- Metadata Management
- Data Governance
Modern Data Stack
- dbt
- Apache Airflow
- Terraform
- Git / GitHub
- Cloud Build
- CI/CD
- Data Quality Frameworks
- OpenLineage is a plus
Benefits
- Lead large‑scale AWS‑to‑GCP cloud transformation initiatives.
- Work on enterprise Data Lakehouse and analytics modernisation projects.
- Flexible remote work environment.
- Exposure to global enterprise customers.
- Collaborative, innovation‑driven engineering culture.
- Continuous learning and certification opportunities.
Join Naveera Technology LLC and play a pivotal role in delivering enterprise‑scale AWS‑to‑GCP cloud transformation programmes. Lead the modernisation of data platforms, ETL pipelines, and analytics ecosystems while driving innovation across cloud‑native data engineering, Data Lakehouse architectures, and next‑generation Business Intelligence solutions.
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