In this role, you will be the backbone of our data infrastructure, architecting robust pipelines that transform raw financial data into actionable insights for our wealth management and mutual fund business units.

You will collaborate closely with quantitative analysts, product managers, and business stakeholders to ensure data integrity and availability.

Your work will directly influence critical investment decisions and enhance the digital experience for our clients, ensuring that our data ecosystem remains scalable, secure, and performant in a fast-paced capital markets environment.

Key Responsibilities :

- Design and maintain scalable ETL/ELT pipelines using Airflow to ensure seamless data ingestion from diverse financial sources, enabling real-time reporting for portfolio managers.

- Develop and optimize complex SQL queries and data models within BigQuery to support high-frequency trading analytics and client-facing wealth management dashboards.

- Implement modular data transformations using DBT to improve code maintainability and data quality, ensuring accuracy for regulatory reporting and mutual fund performance tracking.

- Partner with cross-functional teams to translate complex business requirements into technical data specifications, driving efficiency in our investment research workflows.

- Monitor and troubleshoot data pipeline performance, proactively identifying bottlenecks to maintain high availability for mission-critical financial applications.

Required Skillset :

- Demonstrated expertise in building end-to-end data pipelines using Airflow and managing large-scale data warehouses in BigQuery, with a deep understanding of cloud-native data architectures.

- Advanced proficiency in writing complex SQL for data manipulation and performance tuning, specifically within the context of financial datasets and capital market instruments.

- Proven ability to leverage DBT for version-controlled data modeling, ensuring that data transformation logic is transparent, testable, and scalable.

- Strong analytical mindset with the ability to communicate technical data challenges to non-technical stakeholders, fostering a collaborative environment across global teams.

- Ability to thrive in a hybrid work setup, demonstrating self-motivation and the capacity to manage complex projects independently while adhering to strict financial industry compliance standards.

- A Bachelors or Masters degree in Computer Science, Engineering, or a related quantitative field, backed by 5 - 10 years of professional experience in data engineering roles.