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

Naveera Technology LLC

1d•Remote

About The Position

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.

Requirements

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 modeling 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.
Strong ability to translate complex business and technical requirements into scalable, secure and cost-effective GCP architectures.
Ability to manage, mentor and technically guide Data Engineering teams while remaining hands-on with critical architecture and implementation decisions.
Strong expertise in Data Lakehouse, Data Warehouse, Streaming, Data Modeling and modern data engineering patterns.
Ability to optimize BigQuery, Dataflow and Spark workloads while implementing cloud cost optimization strategies.
Strong understanding of data quality, lineage, metadata, IAM, encryption, access control and enterprise governance.
Excellent communication skills with the ability to work directly with US-based business and technical stakeholders.
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 optimization.
Data Architecture: Enterprise Data Lake / Lakehouse, Medallion Architecture, Data Modeling, Dimensional Modeling, Multi-tenant Data Modeling, Schema-on-Read / Schema-on-Write, Data Lineage, Metadata Management, Data Governance, Modern Data Stack.
Tools: dbt, Apache Airflow, Terraform, Git / GitHub, Cloud Build, CI/CD.
Data Quality Frameworks.
OpenLineage is a plus.

Nice To Haves

Experience with Dataplex, Data Catalog and data lineage is preferred.

Responsibilities

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.
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.
Analyze and optimize existing AWS data platforms before migration.
Work with AWS services like S3, Glue, Glue Data Quality, Redshift / Redshift Serverless, Athena, Step Functions, DMS, 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.
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.
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.
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.
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.
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.
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.
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.
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.