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Mid-level generalist data role, metro locations, and broad GCP skillset create high candidate competition.
Core cloud data engineering skills are easily transferable across industries, so background fit sensitivity is low.
Explicit 3–6 year requirement plus mandatory GCP, BigQuery, Dataflow, SQL and Python skills increases shortlisting strictness.
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Develop and maintain scalable batch and real-time data pipelines on Google Cloud Platform supporting enterprise analytics and AI use cases.
Build reusable, domain-oriented data products and implement data quality, schema management, and metadata enrichment to ensure reliable production-ready data pipelines.
Collaborate with analytics, AI/ML teams, and architects to support semantic modeling, AI-ready data ecosystems, and operational stability of cloud-native data solutions.
3–6 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 Google Cloud Platform data services including BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, and Cloud Composer.
Strong SQL and Python programming skills and experience developing scalable ETL/ELT pipelines and distributed data processing workflows.
Experience working on enterprise-scale data modernization and AI/ML-enabled data ecosystems with exposure to semantic layers and BI/reporting platforms.
Comfortable working in agile, cross-functional teams collaborating with architects, lead engineers, analytics, and AI/ML stakeholders.
Familiarity with cloud-native data engineering best practices, DevOps automation, monitoring frameworks, and operational support for complex GCP data platforms.