Data Engineer - Data build tool

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 Big Query  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 Big Query  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 .