Hiring Data Engineers with data build tool experience for big4 firm in Gurgaon location. Data Engineer – dbt Cloud & Airflow Role Overview We are looking for an experienced Data Engineer with strong hands-on expertise in dbt Cloud and Apache Airflow to design, develop, and maintain scalable data transformation and orchestration solutions. The ideal candidate should have practical experience working with GCP BigQuery and exposure to the Financial Services (FS) domain . The role requires strong problem-solving skills and the ability to design solutions for real-world data engineering challenges, with a focus on data quality, orchestration, scalability, maintainability, and reliable data pipelines . Key Responsibilities Design, develop, and maintain scalable data pipelines using dbt Cloud and Apache Airflow . Implement complex data transformation workflows using dbt . Develop and maintain dbt Macros, Snapshots, Tests, Hooks, and Seeds based on project requirements. Design and implement Airflow DAGs for efficient data orchestration and dependency management. Work with Airflow architecture, Sensors, Operators, and orchestration patterns . Implement Airflow Dynamic Task Mapping to improve scalability and pipeline efficiency. Work with GCP BigQuery for data processing, transformation, storage, and analytics. Implement data quality and validation frameworks using dbt tests and other validation mechanisms . Handle Slowly Changing Dimensions (SCD Type 2) and historical data tracking. Develop strategies for handling late-arriving and out-of-sequence data . Define appropriate dbt implementation strategies based on project architecture and business requirements. Troubleshoot complex data pipeline issues and identify effective solutions. Collaborate with business and technical stakeholders to understand requirements and translate them into robust data solutions. Ensure data pipelines are scalable, maintainable, reliable, and optimized for performance. Participate in solution design, architecture discussions, and technical decision-making. Contribute to production support, monitoring, troubleshooting, and continuous improvement of data engineering processes. Good to Have Experience in the Financial Services (FS) domain . Experience in Investment Management or related financial domains. Experience working with large-scale data platforms and enterprise data environments. Exposure to cloud-based data engineering architecture and best practices. Preferred Domain Experience Candidates with experience in Financial Services , particularly Investment Management , will be preferred. However, Investment Management experience is desirable and not mandatory .
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