Responsibilities

• Design, develop, test, deploy, and maintain robust and scalable ELT data pipelines using dbt (data build tool) for data transformation within Snowflake.

• Orchestrate and schedule complex data workflows using Apache Airflow, ensuring timely and reliable data delivery.

• Develop connectors and scripts (primarily in Python) to extract data from various source systems (APIs, databases, files, streaming platforms) and load it into Snowflake.

• Implement data ingestion strategies (batch and streaming) using Snowflake's capabilities (e.g., Snowpipe, external stages).

• Optimize Snowflake warehouse usage, query performance, and overall data platform efficiency.

• Manage and monitor Snowflake resources, ensuring cost-effectiveness and scalability.

• Implement and enforce data governance, security (e.g., RBAC, data masking), and privacy best practices within Snowflake.

• Assist in schema design, table optimization (clustering, partitioning), and data loading strategies.

• Solve key business problems through using an appropriate mix of strategic thinking and computational methods.

• Develop and uphold best practices with respect to change management, documentation and data protocols.

Requirements

• Bachelor/Master degree in Analytics, Data Science, Mathematics, Computer Science, Information Systems, Computer Engineering, or related technical field.

• Demonstrated mastery of complex SQL queries, analytical functions, stored procedures, and performance tuning.

• 5+ years of hands-on experience with SQL or any Data warehouse/Data Lake, including data loading, transformations, performance optimization, and security features.

• Proven experience in building and managing complex data transformation pipelines using dbt, including Jinja templating, macros, tests, and documentation.

• Solid experience in designing, developing, and deploying production-grade data pipelines using Apache Airflow (DAGs, Operators, Sensors, XComs).

• Strong Python scripting skills for data manipulation, API integrations, and Airflow DAG development.

• Analytical and independent problem solver. Meticulous with high attention to detail.

• Strong communicator with ability to switch hats between data/technical speak and business/layperson speak.

• Solid understanding of data warehousing concepts, dimensional modeling (star/snowflake schemas), and data lake architectures.

• Deep understanding of Extract, Load, Transform (ELT) or ETL principles and best practices.

• Familiarity with data quality frameworks, data lineage, and data governance principles.

• Experience working in a digital banking or financial services environment is highly advantageous, with an understanding of financial data concepts and regulatory requirements.