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Mid-level metro data engineer at a known brand with popular title increases candidate competition.
Core GCP data engineering skills are highly transferable across industries.
Explicit 3–6 years requirement and specific GCP/data stack mandate moderate filter strictness.
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Develop and maintain scalable batch and real-time data pipelines on GCP to support enterprise analytics and AI use cases.
Build reusable, cloud-native data engineering components and products following architectural standards.
Support monitoring, governance, and operational stability of data pipelines within enterprise data modernization and AI ecosystems.
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, Pub/Sub, and Cloud Storage.
Strong skills in SQL and Python programming; experience with ETL/ELT pipelines and distributed data processing workflows.
Experienced in enterprise-scale data modernization and familiar with semantic data modeling and analytics enablement.
Comfortable working with GCP-native tools and AI/ML data pipelines, including Vertex AI and BigQuery ML.
Operates well in Agile, cross-functional delivery teams, collaborating with architects, engineers, and analytics stakeholders in fast-paced environments.