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Metro location and generalist data engineer title increase competition, while seniority and domain specialization moderate it.
Core data engineering skills are transferable, but financial domain and vendor-feed experience increase domain specificity.
Explicit 9+ years and required dbt/BigQuery/financial-data expertise make filters strict.
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Design, build, and maintain scalable ELT pipelines using Python, SQL, dbt, and Apache Airflow on Google Cloud Platform to process large-scale financial datasets.
Develop and enforce data modelling standards, data quality checks, reconciliation processes, and performance optimization strategies for a cloud-native data platform.
Collaborate across teams to translate business requirements into robust data solutions and support CI/CD, monitoring, and disaster recovery for critical data infrastructure.
9+ years of professional experience in data engineering or related roles.
Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
Strong expertise in Python, SQL, data modelling, and cloud data warehouses (preferably BigQuery).
Experience in financial services or fintech domains involving securities master data, corporate actions, or market data vendor feeds.
Expertise in building and modernizing financial data platforms with a focus on data pipeline reliability, data quality, and performance optimization.
Ability to design and implement complex ELT pipelines, data transformations, and validation frameworks using modern data stack tools (dbt, BigQuery, Airflow).
Experience working in cross-functional, globally distributed teams delivering enterprise-grade data solutions for financial datasets.