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Senior, niche modern-data-stack role in Gurgaon at a reputable firm yields moderate applicant density.
Core data engineering skills transfer broadly, but finance-specific data and vendor-feed experience increases sensitivity.
Explicit 9+ years and mandatory dbt/BigQuery/Airflow/Python requirements create high filtering.
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Design and build scalable, reliable ELT pipelines and data models for large financial datasets using dbt, BigQuery, and Apache Airflow on Google Cloud.
Implement automated data quality, reconciliation processes, and monitoring to ensure data accuracy and platform health.
Collaborate across teams to translate business requirements into technical solutions and continuously improve data platform capabilities including APIs and CI/CD pipelines.
9+ years of experience in data engineering, database development, or a related role.
Strong expertise in data modelling (dimensional modelling, slowly changing dimensions) and advanced SQL for large datasets.
Hands-on experience with dbt, BigQuery (or willingness to work on BigQuery), Python, and workflow orchestration tools like Apache Airflow/Cloud Composer.
Experience in financial services or fintech domain, especially with securities master data, corporate actions, market data vendor feeds.
Experienced in modern data platform modernization and cloud-native ELT design patterns focusing on reliability and performance.
Strong familiarity with data governance practices including lineage, cataloguing, and data quality management in a financial data context.
Comfortable working in globally distributed teams translating complex financial domain concepts into robust data engineering solutions.