Data Engineer (financial/investment domain)

at  Luxoft

PROJECT DESCRIPTION

We are seeking a highly skilled and experienced Data Engineer to join our Data & Analytics team. The ideal candidate will have a strong background in Snowflake SQL, investment reporting data, and financial knowledge related to investment management. As a Data Engineer, you will play a critical role in designing, implementing, and maintaining our data infrastructure to support investment reporting and analytics.

SKILLS

Must have
Education:
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field
Experience:
Minimum of 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.
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 programming languages such as Python.
Knowledge of data governance, security, and compliance best practices.
Excellent problem-solving and analytical skills.
Strong communication and collaboration skills.
Nice to have
N/A

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.