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

Naveera Technology LLC is a trusted global engineering partner delivering Data Engineering, Generative AI, Application Development, and IT Infrastructure solutions. They are seeking an experienced Engineering Manager/Solutions Architect to lead the architecture and execution of a large-scale GCP-to-AWS data platform migration, focusing on designing enterprise data platforms and cloud migration strategies.

Responsibilities:

• 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
• Architect and implement end-to-end streaming solutions using: Google Pub/Sub, Dataflow / Apache Beam, BigQuery, Cloud Composer / Airflow, 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
• 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, 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
• 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
• 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
• 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, Network and 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
• 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
• Lead large-scale cloud performance optimization initiatives. Optimize: AWS Glue DPU consumption, Redshift distribution and sort keys, S3 storage and Parquet partitioning, Athena query performance, 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
• 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
• 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:

• 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
• 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