Responsibilities

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.

Mandatory Skills Description:

Education:

- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field

Experience:

- 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 model 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.

Skills:

- 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.

Data Engineer (ETL)

Financial/Investment Data Engineer (SQL queries/ETL/DWH)