Data Engineering Manager | GCP to AWS Migration @ Naveera Tech, USA - Remote Work

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.

About the Role

We are looking for an experienced Engineering Manager / Solutions Architect with strong hands-on expertise in GCP and AWS Data Engineering to lead the architecture and execution of a large-scale GCP-to-AWS data platform migration. The ideal candidate will have a strong background in designing enterprise data platforms, cloud migration strategies, streaming and batch data pipelines, data lakehouse architecture, and modern data governance. The role requires someone who can operate at both architectural and engineering leadership levels, working closely with US-based business and technical stakeholders. The candidate should have strong experience in GCP data services such as Pub/Sub, Dataflow, BigQuery, and Cloud Composer, along with deep AWS expertise across S3, Glue, Redshift, Athena, Step Functions, and AWS DMS.

Responsibilities

• Cloud Migration & Architecture
  • Lead the end-to-end migration of data platforms from GCP to AWS.
  • Assess existing GCP architecture, workloads, data pipelines, dependencies, and operational processes.
  • Define the target-state AWS architecture and migration roadmap.
  • Develop migration strategies for BigQuery → Redshift/Snowflake, GCS → S3, Pub/Sub/Dataflow → AWS streaming, and Glue-based processing.
  • Identify opportunities to modernize existing workloads rather than performing simple lift-and-shift migrations.
  • Define architecture standards, design principles, technology selection, and reusable patterns.
  • Lead technical discussions, architecture reviews, and proof-of-concept initiatives.
• GCP Data Engineering & Streaming
  • Architect and implement end-to-end streaming solutions using Google Pub/Sub, Dataflow / Apache Beam, BigQuery, Cloud Composer / Airflow, and Google Cloud Storage.
  • Design real-time ingestion, enrichment, and transformation pipelines for high-volume event data.
  • Optimize streaming pipelines for latency, throughput, scalability, and reliability.
  • Design event-driven architectures with appropriate delivery and processing guarantees.
  • Define BigQuery schema design, partitioning, and clustering strategies.
  • Analyze existing GCP workloads and determine the appropriate AWS equivalent during migration.
• AWS Data Lakehouse Architecture
  • Architect enterprise-grade AWS Data Lakehouse solutions using Bronze, Silver, and Gold/Atomic layers.
  • Design scalable data platforms using Amazon S3, AWS Glue, AWS Glue Data Quality, Amazon Redshift / Redshift Serverless, Amazon Athena, AWS Step Functions, and AWS DMS.
  • Establish data ingestion, transformation, storage, and consumption patterns.
  • Design multi-tenant data models and scalable schema strategies.
  • Implement schema-on-read and schema-on-write approaches where appropriate.
  • Define Parquet-based storage and partitioning strategies for large-scale datasets.
• ETL / ELT & Data Processing
  • Design and optimize batch and real-time ETL/ELT pipelines.
  • Develop scalable transformation frameworks using AWS Glue, PySpark, Python, SQL, and dbt.
  • Design CDC pipelines using AWS DMS from SQL Server and other OLTP systems.
  • Build event-driven workflows using AWS Step Functions and Airflow.
  • Optimize Glue workloads, including DPU utilization and execution performance.
  • Implement advanced dbt models, testing frameworks, and reusable transformation patterns.
  • Perform complex SQL optimization and query-performance tuning.
• Data Modeling & Warehousing
  • Define enterprise data models supporting analytics, BI, and operational reporting.
  • Design dimensional, normalized, and denormalized models based on business requirements.
  • Develop multi-location and multi-tenant data structures.
  • Design Redshift distribution and sort-key strategies.
  • Implement partitioning and clustering strategies across cloud data platforms.
  • Ensure models support both real-time and batch analytics requirements.
  • Work with BI teams to develop scalable semantic and consumption layers.
• Data Governance, Security & Quality
  • Establish enterprise data governance and data quality standards.
  • Implement automated data quality gates using AWS Glue Data Quality and dbt tests.
  • Establish data lineage, metadata management, and data ownership practices.
  • Work with governance teams to ensure data integrity and traceability.
  • Implement secure data architectures using IAM, least-privilege access, encryption, S3 security controls, and network/access controls.
  • Experience with AWS Lake Formation is preferred.
  • Familiarity with OpenLineage and modern data lineage frameworks is preferred.
  • Ensure solutions align with enterprise security and compliance requirements.
• DevOps, IaC & Automation
  • Lead infrastructure automation using Terraform.
  • Build repeatable and secure cloud deployment frameworks.
  • Implement CI/CD pipelines for data engineering workloads.
  • Work with GitHub Actions, Git, and cloud-native deployment tools.
  • Automate infrastructure, data pipelines, testing, and deployment processes.
  • Establish environment management across Development, QA, UAT, and Production.
  • Promote engineering best practices around version control, code reviews, and automated testing.
• Performance & Cost Optimization
  • Lead large-scale cloud performance optimization initiatives.
  • Optimize AWS Glue DPU consumption, Redshift distribution and sort keys, S3 storage and Parquet partitioning, and Athena query performance.
  • Optimize data pipeline execution times.
  • Identify opportunities to reduce AWS infrastructure and processing costs.
  • Develop FinOps and cloud cost optimization strategies.
  • Establish performance benchmarks and SLAs for critical data workloads.
• Engineering Management & Technical Leadership
  • Provide technical leadership and mentoring to data engineers and architects.
  • Lead a team of mid-level and senior data engineering professionals.
  • Conduct architecture/design reviews and establish engineering standards.
  • Define technical roadmaps aligned with business objectives.
  • Break complex requirements into actionable technical deliverables.
  • Review technical designs, code, and implementation approaches.
  • Work with Engineering, DevOps, Data Science, BI, and Product teams.
  • Help build engineering capabilities, reusable frameworks, and best practices.
• Stakeholder & Client Management
  • Act as the primary technical liaison with US-based stakeholders.
  • Work directly with business leaders, product teams, Data Science, and BI teams.
  • Translate business and operational requirements into scalable technical solutions.
  • Present architecture decisions, migration strategies, and technical roadmaps to senior stakeholders.
  • Communicate risks, dependencies, timelines, and technical trade-offs effectively.
  • Support business teams in defining enterprise and clinical/operational KPIs.

Requirements

• Experience & Qualifications
  • 10+ years of experience in Data Engineering, Data Architecture, or Cloud Engineering.
  • 3+ years of hands-on GCP Data Engineering experience.
  • Strong hands-on experience with AWS Data Engineering and Architecture.
  • Proven experience leading or contributing to GCP-to-AWS migration projects.
  • Experience designing large-scale data lake/lakehouse platforms.
  • Strong experience with streaming and event-driven architectures.
  • Expert-level SQL, Python, and PySpark skills.
  • Strong data modeling and data warehousing experience.
  • Experience with Terraform and CI/CD.
  • Proven ability to lead technical teams and mentor engineers.
  • Strong communication skills with the ability to interact directly with US-based stakeholders.
• Preferred Qualifications
  • Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related technical field.
  • AWS Certified Solutions Architect – Professional.
  • AWS Certified Data Engineer – Associate.
  • Google Cloud Professional Data Engineer certification.
  • Experience with Healthcare or Insurance domain.
  • Understanding of HIPAA and healthcare data security requirements is a plus.
  • Experience with Microsoft Fabric and Power BI integration.
  • Experience with Power BI Embedded and cloud-hosted applications.
  • Experience defining healthcare/clinical KPIs, operational metrics, referral metrics, or conversion metrics.
• Required Technical Skills
  • Cloud & Migration
    • GCP: Pub/Sub, Dataflow, BigQuery,