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Mid-level generalist data engineer in metro locations with broad GCP skillset attracts strong competition.
Core cloud data engineering skills transfer across industries, though GCP and analytics context gives moderate domain bias.
Explicit 3–6 years plus mandatory GCP data engineering skills increase filtering but no strict certifications required.
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Develop and maintain scalable, batch and real-time data pipelines on Google Cloud Platform supporting enterprise analytics, AI, and digital transformation.
Contribute to reusable data product development, including data quality, schema management, and transformation frameworks for downstream analytics and AI use cases.
Support GCP platform services operation, monitoring, troubleshooting, engineering best practices, and governance ensuring reliability and compliance.
3 to 6 years of experience in data engineering and cloud-based data platform development.
Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
Hands-on experience with GCP data services (e.g., BigQuery, Dataflow, Pub/Sub) and programming in SQL and Python.
Knowledge of ETL/ELT pipeline development, distributed data processing, and modern data architectures including data lakes, warehouses, and streaming.
Experienced in enterprise-scale data modernization and cloud-native data platform development on GCP.
Familiar with semantic data modeling, AI/ML data enablement (Vertex AI, BigQuery ML), and integrating data ecosystems with analytics and BI tools.
Capable of delivering reliable, reusable, production-ready data solutions collaborating with architects, engineers, and analytics teams in agile environments.