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High due to common Data Engineer title, 3–6 year band, metro location, and a well-known agency brand.
Medium because core data engineering skills transfer across industries but GCP and domain experience increase specificity.
High due to explicit 3–6 years requirement and mandatory GCP/data platform technical skills.
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Develop and maintain scalable batch and real-time data pipelines on Google Cloud Platform to support enterprise analytics, AI, and digital transformation.
Contribute to reusable, domain-oriented data products including data quality, schema management, and metadata enrichment to ensure reliable and production-ready pipelines.
Collaborate with analytics, AI/ML teams, and architects to build semantic models, support AI/ML data enablement, and maintain operational excellence including governance and monitoring.
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 services such as BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, and Cloud Composer (Airflow).
Strong SQL and Python programming skills with experience in scalable ETL/ELT pipeline development and distributed data processing workflows.
Experienced in building and modernizing enterprise-scale data platforms on GCP with focus on data engineering, AI/ML enablement, and semantic data modeling.
Operates effectively in agile, cross-functional teams collaborating closely with architects, analytics, and AI specialists.
Familiar with engineering best practices including CI/CD, DevOps, monitoring, governance, and data product development in a fast-paced enterprise environment.