Google Cloud Data Engineer

Accountabilities

Design, develop, and maintain end-to-end data platforms on Google Cloud, translating business requirements into scalable and reliable technical solutions.
Build, orchestrate, and optimize data pipelines for data ingestion, transformation, validation, and delivery across diverse data sources.
Develop complex SQL queries and Python-based processing solutions to support efficient data transformation, analysis, and integration.
Design resilient data processing workflows that handle late-arriving records, duplicate entries, inconsistent formats, and other real-world data quality challenges.
Develop effective data models and architectures that maintain clarity, consistency, and performance as data volumes and business requirements grow.
Optimize data processing performance, resource utilization, and cloud infrastructure costs while maintaining reliability and scalability.
Implement data quality checks, validation mechanisms, governance practices, and monitoring processes to ensure data accuracy and consistency.
Apply appropriate cloud networking, security, access control, and data protection practices to ensure information remains within approved security and geographic boundaries.
Support data residency, governance, and compliance requirements, particularly for clients operating in regulated industries.
Collaborate with clients, Google Cloud teams, and other technical stakeholders to gather requirements, explain architectural trade-offs, and align technical solutions with business objectives.
Troubleshoot pipeline failures, investigate data discrepancies, and continuously improve the reliability and efficiency of production data systems.
Take end-to-end ownership of delivery, from solution design and implementation to deployment, maintenance, and ongoing optimization.
Requirements
Between 3 and 10 years of experience in data engineering, with hands-on experience designing, building, and maintaining data solutions on Google Cloud. Technical depth and demonstrated expertise are valued more highly than years of experience alone.
Strong proficiency in SQL, including complex queries, data transformations, performance tuning, and efficient data processing.
Advanced Python skills for developing data pipelines, implementing processing logic, automating workflows, and building maintainable engineering solutions.
Practical experience with Google Cloud Platform (GCP) services and tools used to build, deploy, and operate data platforms.
Solid understanding of data engineering fundamentals, including data ingestion, transformation, pipeline orchestration, data modeling, and distributed processing concepts.
Experience designing reliable data pipelines that account for late-arriving data, duplicate records, incomplete datasets, and inconsistent source information.
Strong knowledge of data modeling principles and the ability to design structures that remain understandable, maintainable, and performant as datasets expand.
Understanding of data quality, governance, security, and compliance, including data residency requirements and the protection of sensitive information.
Familiarity with cloud networking, access management, and security controls sufficient to maintain secure data flows and enforce infrastructure boundaries.
Ability to identify performance bottlenecks, investigate complex data issues, and balance processing efficiency, reliability, and infrastructure costs.
Excellent analytical, problem-solving, and communication skills, with the ability to explain technical decisions and trade-offs clearly to clients and non-technical stakeholders.
A strong sense of ownership, attention to detail, and the ability to work independently while collaborating with distributed technical teams.
Availability for approximately six hours of overlap with North American Eastern Time to facilitate collaboration with clients and project teams.
A Professional Google Cloud certification is a plus.
Experience supporting enterprise clients, regulated industries, or large-scale data platforms is an advantage.
Benefits
Remote work: Work remotely from India, with the flexibility of a distributed working environment.
Flexible engagement options: Opportunity to work on a contract or full-time basis, depending on the agreed arrangement.
Competitive compensation: Compensation aligned with your experience, technical expertise, and employment arrangement.
Google Cloud collaboration: Contribute to projects referred through Google and work alongside Google Cloud teams.
Large-scale technical challenges: Work on complex data platforms and engineering initiatives across multiple industries.
Professional growth: Expand your expertise in Google Cloud data engineering, scalable architectures, data governance, and pipeline optimization.
Real-world impact: Build and maintain data platforms that clients depend on for their everyday operations and business decisions.
Technical ownership: Take responsibility for data solutions throughout their lifecycle, from architecture and development to production support and continuous improvement.
Cross-industry exposure: Gain experience addressing diverse data engineering challenges, including the requirements of regulated environments.
Collaborative environment: Work with technical professionals and clients in an international setting, with opportunities to exchange knowledge and develop new skills.