Design, develop, and optimize scalable cloud-native data platforms and pipelines on GCP.
Implement robust batch, streaming, and event-driven data processing solutions supporting enterprise analytics and AI use cases.
Collaborate with Enterprise Architects to translate target-state architecture into scalable engineering implementations.
Develop reusable and domain-oriented data products aligned with data mesh and data-as-a-product principles.
Support implementation of semantic and business-consumption layers that simplify enterprise data access.
Build AI-ready data pipelines and scalable feature engineering workflows supporting enterprise AI initiatives.
Lead day-to-day engineering activities across multiple data engineering workstreams.
Implement data governance, lineage, monitoring, and observability frameworks.

Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field.
7+ years of experience in data engineering and cloud-native data platform development.
Minimum 4+ years of hands-on experience delivering enterprise-scale solutions on GCP.
Strong expertise in building scalable batch and streaming data pipelines.
Experience working on modern enterprise data platforms supporting analytics, AI/ML, and GenAI use cases.
Good understanding of semantic layer concepts, reusable data models, and governed data consumption patterns.
Experience working within large-scale data modernization and cloud transformation initiatives.
Strong problem-solving, debugging, and performance optimization skills.
Proven ability to lead engineering teams and collaborate across architecture, product, and business functions.
Excellent communication and stakeholder management skills.
GCP certifications such as Professional Data Engineer preferred.

Associate Technical Architect

Key skills