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Senior GCP-specific role in a metro reduces applicants despite data engineer popularity.
Core data engineering skills are broadly transferable across industries despite GCP specificity.
Explicit 8–12 years plus mandatory GCP, BigQuery, Airflow, dbt and Python requirements.
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Lead design and development of end-to-end high-performance data pipelines and data products using GCP technologies to meet business and system requirements.
Collaborate closely with stakeholders to translate data needs from structured and unstructured sources into scalable, fit-for-use data architectures and solutions.
Mentor the data engineering team, drive continuous process improvements, and ensure high data quality and security compliance in data delivery workflows.
8-12 years of experience in data engineering.
Proficient with Google Cloud Platform services including Cloud Run, BigQuery, Cloud SQL for PostgreSQL, Cloud Spanner, Cloud Storage, Managed Airflow (Cloud Composer), Pub/Sub, and IAM permission management.
Strong programming skills in Python and advanced skills in cloud data pipeline architecture (preferably GCP).
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Software Engineering, or a related field.
Experienced in architecting and managing complex distributed data pipelines and transformations within customer-facing environments using GCP.
Strong analytic problem-solving skills demonstrated by resolving complex data challenges and designing robust data models and pipelines.
Able to lead and mentor a globally distributed team, maintain detailed documentation, and implement continuous improvement practices in data engineering processes.