Data Engineer (financial/investment domain)

Data Infrastructure Development:

Design, implement, and maintain scalable and efficient data pipelines using Snowflake and SQL.

Develop data models, ETL processes, and data integration workflows to ensure high data quality and reliability.

Optimize data storage and retrieval performance in Snowflake.

Data Manipulation and Transformation:

Perform complex data manipulation and transformation using SQL to prepare data for analysis and reporting.

Implement data cleansing, aggregation, and enrichment processes to ensure data accuracy and consistency.

Develop and maintain reusable SQL scripts and stored procedures for data processing.

Collaboration and Support:

Collaborate with data analysts and other stakeholders to understand data requirements and deliver solutions that meet their needs.

Provide technical guidance and mentorship to junior data engineers and other team members.

Work closely with IT and DevOps teams to ensure seamless data integration and deployment.

Data Layer Preparation:

Design and build data layers to support various analytical and reporting needs.

Ensure data layers are well-documented, easily accessible, and performant.

Implement data governance and security best practices to protect sensitive information.

Performance Tuning and Optimization:

Monitor and optimize the performance of data pipelines, databases, and queries.

Identify and resolve performance bottlenecks to ensure timely and efficient data processing.

Stay up-to-date with the latest trends and best practices in data engineering and apply them to improve our data infrastructure.