Financial/Investment Data Engineer (SQL queries/ETL/DWH)
Summary
Build and optimize Snowflake-based data pipelines and ETL workflows to power financial reporting and investment analytics using SQL and cloud data warehousing.
Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
- Minimum of 2-5 years of experience in data engineering, with a strong focus on SQL and data manipulation.
- Proven experience working with Snowflake or other cloud‑based data warehousing solutions.
- Experience with Tableau.
- Strong background in designing data models and building scalable data pipelines and data models.
- Solid understanding of financial instruments, investment operations, investment reporting and portfolio management concepts.
- Knowledge of investment performance measurement, risk analysis, and portfolio management concepts.
- Expert‑level proficiency in SQL, with the ability to write complex queries and optimize them for performance.
- Strong experience with ETL tools and processes.
- Familiarity with Tableau.
- Knowledge of data governance, security, and compliance best practices.
- Excellent problem‑solving and analytical skills.
- Strong communication and collaboration skills.