Equities Data Engineer

A superb opportunity for a Data Engineer with this leading financial institution. The Data Engineer will be a crucial member ensuring efficient data management and analysis within the Global Equities Division.

Responsibilities include:

1. Data Engineering Solutions:

   As a Data Engineer, you'll create and manage ETL (Extract, Transform, Load) processes. These processes are essential for extracting data from various sources, transforming it into a usable format, and loading it into databases or data warehouses. REST-oriented APIs play a significant role in data integration. You'll design and maintain APIs that allow seamless communication between different systems and applications.

2. Database Maintenance and Cleaning:

   Ensuring the cleanliness and integrity of databases is critical. You'll be responsible for cleaning, organizing, and maintaining the department's various databases. This includes removing duplicate records, handling missing data, and optimizing database performance.

3. Automation:

   Automating existing processes improves efficiency. You'll develop scripts or workflows to automate repetitive tasks, reducing manual effort and minimizing errors.

4. Data Quality and Governance:

   High data quality is essential for accurate analysis. You'll implement data quality checks, monitor data consistency, and enforce data governance policies. Strong data governance ensures compliance with regulations and internal standards. You'll contribute to defining and maintaining data governance practices.

5. Advanced Analytics and Data Science Applications:

   Collaborating with the Equity Data Science Team, you'll contribute to the development, testing, deployment, and documentation of advanced analytics and data science applications. These applications aid the team's investment decisions by providing insights and predictive models based on historical and real-time data.

The successful Data Engineer will be Degree educated in Technology, Science, Engineering or Finance and have experience of data engineering for automatic end-to-end data collection, transformation, and processing using tools (Airflow, Azure Data factory, Microsoft SSIS or similar) OOP languages, e.g., Python, Java, .Net, and C# cloud platforms (Snowflake, Microsoft Azure or similar) SQL and Relational Database development (ideally SQL Server) Business Intelligence design and implementation, (Tableau or Microsoft Power BI preferred) Experience of docker, Kubernetes, MongoDB, Streamlit would be advantageous.

Overall, this role combines technical expertise, analytical skills, and a deep understanding of financial data. If you're passionate about data engineering and equities, this opportunity could be an exciting fit!