Software Engineering Lead – Investment Analytics, VP

• Design, build, and optimize scalable applications and data pipelines using Python, PySpark, and SQL on Databricks (AWS)
• Partner with business stakeholders to understand financial data, analytics requirements, and downstream usage across research, portfolio management, risk, and reporting
• Implement solutions that support financial analytics use cases, including (but not limited to) performance measurement, attribution, returns analysis, and benchmark comparisons
• Participate in requirements analysis, solution design, and technical implementation following an Agile SDLC
• Develop robust, maintainable data workflows with strong emphasis on data quality, transparency, and operational reliability
• Optimize Spark and SQL workloads for scale and performance using appropriate partitioning, query design, and Databricks best practices
• Own the investigation and resolution of complex production issues, including root-cause analysis and long-term remediation
• Collaborate with application, data architecture, and infrastructure teams to deliver end-to-end solutions
• Contribute to technical standards, best practices, documentation, and design reviews
• Remain current with emerging technologies and recommend improvements to tools, frameworks, and development practices

• 10+ years of experience delivering technology solutions, preferably within asset management or financial services
• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience
• Strong written and verbal communication skills
• Strong experience working with financial data in an asset management or capital markets environment
• Advanced development skills in:
  Python, PySpark, and advanced SQL
• Experience building solutions on Databricks / Apache Spark in a cloud environment (AWS preferred)
• Solid understanding of data warehousing and analytical data platform concepts
• Strong analytical and problem-solving skills with the ability to work independently.

Demonstrates expertise in designing and optimizing scalable applications and data pipelines using Python, PySpark, and SQL on Databricks, with a strong focus on financial data analytics and operational reliability. Proven ability to collaborate with stakeholders and deliver end-to-end solutions in an Agile environment.

• Python Development
• PySpark Application Development
• SQL Optimization
• Databricks Solutions Architecture
• Financial Data Analytics

• Python
• Py Spark
• SQL
• Data Pipeline Development
• Data Quality Assurance
• Root-Cause Analysis
• Agile SDLC
• Data Warehousing
• Analytical Data Platforms
• Performance Measurement

• Strong Communication Skills
• Analytical Problem-Solving
• Collaboration

• Asset Management
• Financial Services
• Capital Markets
• Performance Attribution
• Benchmark Comparisons

• Databricks
• AWS
• Apache Spark