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Mid-level popular data role, metro location, and broad cloud stack make applicant competition high.
Core data engineering skills (ETL, SQL, Python, cloud) are broadly transferable across industries, so sensitivity is low.
Mandatory 3+ years plus specific BigQuery, DBT, Python, and Airflow requirements make shortlisting strict (high).
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Lead design and development of scalable, secure BigQuery-based data integration pipelines for structured and semi-structured enterprise data.
Build and maintain analytics-ready data transformation pipelines using DBT, Python, SQL, and automate workflows using Airflow/Cloud Composer.
Mentor junior engineers, establish best practices, and collaborate across teams to deliver trusted, business-aligned datasets.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related technical field.
Minimum 3+ years of hands-on data engineering experience with cloud data warehousing and analytics.
Expertise in Google BigQuery, Python, SQL, DBT, and Airflow/Cloud Composer required.
Work Experience Required: Minimum 3+ years
Experienced in building enterprise-grade ETL/ELT pipelines and scalable data architectures using modern cloud platforms.
Strong understanding of data quality frameworks, pipeline orchestration, and Agile software development practices.
Familiarity with additional tools like Ascend.io, Databricks, Fivetran, Dataflow, and real-time processing platforms is a plus.