Cloud Data Engineer — Snowflake BigQuery Migration

Remote Jobs; United States; Remote; USD 125,000 - 190,000; Full time

Job summary

Remote Jobs is seeking an experienced Senior Data Engineer / Data Migration Architect to lead large-scale enterprise data platform modernization on Google Cloud. You will drive migrations of hundreds of terabytes to BigQuery, design scalable data architectures, and deliver production-grade analytics platforms.

Ideal candidates have Snowflake-to-BigQuery migration experience, strong data engineering and cloud architecture skills, and proven leadership in customer-facing projects.

Qualifications

• Minimum 5 years of experience designing, developing, and delivering high-performance data pipelines for both streaming and batch processing workloads.
• Proven experience migrating large-scale enterprise data warehouses to BigQuery, preferably from Snowflake.
• Experience with data warehouse environments measured in hundreds of terabytes.
• Strong expertise in BigQuery architecture, administration, optimization, and operations.
• Demonstrated experience building and maintaining secure, reliable, and scalable data lakes and data warehouses on Google Cloud Platform.
• Proven ability to design enterprise cloud solutions and lead customer projects through successful delivery.
• Hands-on experience with infrastructure automation, DevOps practices, and CI/CD implementation.
• Google Cloud Professional Data Engineer certification.
• Fluent English communication skills, both written and verbal.

Responsibilities

• Lead the assessment, design, and execution of large-scale data warehouse migrations to BigQuery.
• Define migration strategies, data models, governance frameworks, and performance optimization approaches.
• Drive modernization initiatives that improve scalability, reliability, cost efficiency, and operational excellence.
• Collaborate with stakeholders to ensure seamless migration with minimal business disruption.
• Design, develop, and maintain high-performance data pipelines supporting both batch and streaming workloads.
• Architect secure, scalable data lakes and data warehouse solutions on Google Cloud Platform.
• Establish enterprise-grade data processing frameworks, integration patterns, and governance standards.
• Ensure data quality, observability, lineage, and operational reliability across the data ecosystem.
• Serve as the technical expert for BigQuery architecture, performance optimization, and best practices.
• Implement advanced SQL optimization techniques to improve query performance and reduce costs.
• Design efficient partitioning, clustering, storage, and workload management strategies.
• Automate infrastructure provisioning and platform deployment using IaC; design CI/CD pipelines.
• Promote DevOps best practices across development and operations teams.
• Partner with Data Scientists, Analysts, and Engineers to deliver end-to-end data solutions.

Tools

BigQuery

Snowflake

GCP

IaC

CI/CD

Job description

Role Overview

We are seeking an experienced Senior Data Engineer / Data Migration Architect to lead large-scale enterprise data platform modernization initiatives on Google Cloud. The ideal candidate will have a proven track record of migrating large-scale data warehouses (hundreds of terabytes) to BigQuery, designing high-performance data architectures, and delivering production-grade analytics platforms. Experience migrating from Snowflake to BigQuery is highly preferred; candidates with migration experience from other platforms must demonstrate strong exposure to Snowflake through other engagements.

Key Responsibilities

Data Platform Modernization & Migration

• Lead the assessment, design, and execution of large-scale data warehouse migrations to BigQuery, including environments exceeding hundreds of terabytes of data.
• Define migration strategies, data models, governance frameworks, and performance optimization approaches.
• Drive modernization initiatives that improve scalability, reliability, cost efficiency, and operational excellence.
• Collaborate with stakeholders to ensure seamless migration with minimal business disruption.

Data Engineering & Architecture

• Design, develop, and maintain high-performance data pipelines supporting both batch and streaming workloads.
• Architect secure, scalable, and highly available data lakes and data warehouse solutions on Google Cloud Platform.
• Establish enterprise-grade data processing frameworks, integration patterns, and governance standards.
• Ensure data quality, observability, lineage, and operational reliability across the data ecosystem.

BigQuery Engineering & Optimization

• Serve as the technical expert for BigQuery architecture, performance optimization, and best practices.
• Implement advanced SQL optimization techniques to improve query performance and reduce operational costs.
• Design efficient partitioning, clustering, storage, and workload management strategies.
• Optimize data processing pipelines for performance, scalability, and cost efficiency.

Cloud Architecture & Delivery Leadership

• Design and implement enterprise cloud architectures aligned with business and technical requirements.
• Lead customer-facing projects from solution design through implementation and production deployment.
• Provide technical leadership, mentoring, and architectural guidance to engineering teams.
• Support client workshops, architecture reviews, and strategic roadmap discussions.

DevOps, Automation & Platform Engineering

• Automate infrastructure provisioning and platform deployment using Infrastructure as Code (IaC) practices.
• Design and maintain CI/CD pipelines supporting data platform development and deployment.
• Implement monitoring, alerting, testing, and operational automation capabilities.
• Promote DevOps best practices across development and operations teams.

Cross-Functional Collaboration

• Partner closely with Data Scientists, Data Analysts, Software Engineers, and Business Stakeholders to deliver end-to-end data solutions.
• Support advanced analytics, machine learning, and data-driven decision-making initiatives.
• Translate business requirements into scalable technical solutions and actionable delivery plans.

Mandatory Qualifications

• Minimum 5 years of experience designing, developing, and delivering high-performance data pipelines for both streaming and batch processing workloads.
• Proven experience migrating large-scale enterprise data warehouses to BigQuery, preferably from Snowflake.
• Experience with data warehouse environments measured in hundreds of terabytes.
• Strong expertise in BigQuery architecture, administration, optimization, and operations.
• Demonstrated experience building and maintaining secure, reliable, and scalable data lakes and data warehouses on Google Cloud Platform.
• Proven ability to design enterprise cloud solutions and lead customer projects through successful delivery.
• Hands-on experience with infrastructure automation, DevOps practices, and CI/CD implementation.
• Google Cloud Professional Data Engineer certification.
• Fluent English communication skills, both written and verbal.

Preferred Qualifications

• Fluent German language skills.
• Experience in technical consulting and customer-facing advisory roles.
• Advanced SQL tuning expertise focused on performance optimization and cost efficiency.
• Experience designing and implementing streaming data pipelines with a strong understanding of event delivery semantics and distributed processing concepts.
• Proven experience building production-grade, internet-scale Big Data solutions using managed cloud services.
• Hands-on experience with Snowflake architectures and migration methodologies.
• Experience with Ab Initio is considered an advantage.
• Strong collaboration experience with Data Scientists, Data Analysts, and Software Engineering teams in enterprise environments.

Preferred Technical Expertise

• Google Cloud Platform (GCP)
• BigQuery
• Data Lake and Data Warehouse Architecture
• Snowflake
• Data Migration & Modernization Programs
• Streaming Data Platforms
• Infrastructure as Code (IaC)
• CI/CD and DevOps Tooling
• SQL Performance Tuning
• Data Governance, Security, and Compliance
• Enterprise Data Architecture
• Analytics and Machine Learning Enablement