Data Engineer

Data Strategies, Architecture, and Design:

1. Develop data strategies that will help DWM manage its data and generate useful analytical and managerial reports for various stakeholders across the firm (e.g., Private Credit, Risk Management, Business Analytics, Operations, Fund & Portfolio Monitoring, Impact Analysis & Reporting)

2. Define and implement data architecture strategies to ensure scalability, reliability, and performance in order to delivery on the data strategies developed in point 1 above.

3. Collaborate with DWM stakeholders in Private Credit, Risk Management, Fund Management, Impact Management, and Operations to understand data requirements and translate them into effective models and structures.

Data Pipeline Development:

1. Collaborate with third-party developers of DWM s ETL (Extract, Transform, Load) processes and data pipelines.

2. Implement best practices for data ingestion, transformation, and integration to support analytics and reporting needs across multiple departments at DWM.

Data Quality and Governance:

G-square will see

1. Establish and enforce data quality standards and governance processes to ensure accuracy, consistency, and reliability of data.

2. Collaborate with relevant DWM stakeholders to address data quality issues and continuously improve data management practices.

Performance Optimization: [this is G-Square]

1. Monitor and optimize the performance of data systems to ensure efficient processing and timely delivery of data to end-users.

2. Troubleshoot and resolve performance issues.

Technology Stack Management:

1. Stay abreast of emerging technologies and trends in the data engineering space.

2. Evaluate, select, and recommend (and implement) appropriate tools and technologies to enhance DWM s data engineering ecosystem.

Collaboration and Communication:

1. Work closely with cross-functional teams, including data analysts, and business leaders in Private Credit, Risk Management, Fund Management, Impact Management, and Operations to understand data requirements and deliver effective solutions.

2. Communicate technical concepts and solutions to non-technical stakeholders in a clear and understandable manner.

Qualifications

1.     Advanced degree in Applied Maths, Statistics, Engineering or equivalent
   

2.     5+ years of experience with SQL and NoSQL databases
   

3.     5+ years of experience in software development
   

4.. Exceptional problem-solving and critical thinking skills

5.     Good communication skills and ability to present technical concepts and results to non-technical teams
   

6.     Strong English communication skills
   

7.     Preferred technical skills: Python, .NET, SQL Server, AWS, React, Alteryx, Excel