Accountabilities

Team Leadership and Development: Lead, mentor, and manage a team of Portfolio Analytics Architects, balancing managerial responsibilities with hands-on project delivery. Establish clear objectives, performance metrics, and priorities while ensuring consistent productivity and high-quality outcomes.

Performance Management: Conduct regular one-to-one meetings, performance reviews, and career development discussions. Support employee onboarding, identify development opportunities, delegate responsibilities, and foster a culture of accountability and continuous improvement.

Recruitment and Resource Planning: Assess staffing requirements and collaborate with recruitment teams to attract, interview, select, and onboard qualified professionals.

Data Analytics and Validation: Oversee end-to-end data validation projects involving complex investment portfolios, ensuring accuracy, consistency, and reliable results. Develop scalable data analytics processes using Databricks and create custom Python solutions to detect large-scale data anomalies and automate remediation.

Process Optimization and Automation: Identify opportunities to streamline workflows, improve internal tools, and increase operational efficiency. Introduce AI-driven automation and Large Language Model (LLM) integrations to support scalable implementation processes.

Strategic Leadership: Drive internal management initiatives and contribute to the development and execution of the Data Analytics practice strategy. Collaborate with regional leaders to improve team performance, operational consistency, and service delivery.

Project and Stakeholder Management: Coordinate multiple projects simultaneously, manage priorities, monitor execution, and ensure deadlines are met. Communicate complex findings and project updates clearly while escalating risks and resolving delivery challenges.

Best Practices and Quality Assurance: Establish and promote consistent methodologies, technical standards, and best practices to ensure reliable, high-quality data solutions across projects and teams.

Requirements

Education: Bachelor's degree in Finance, Mathematics, Statistics, Engineering, Business, Economics, or a related discipline. Progress toward CFA or FRM certification is an advantage.

Professional Experience: At least 5 years of experience in technology, finance, consulting, fund accounting, or investment operations, with demonstrated experience managing teams, projects, and cross-functional initiatives.

Financial Data Expertise: Hands-on experience working with complex investment datasets, including multi-asset-class portfolios, investment performance data, and market data from multiple sources.

Technical Skills: Proficiency in Python, Spark, and/or SQL, with a solid understanding of ETL fundamentals and working knowledge of REST APIs.

Data Platforms: Experience with Databricks or other cloud-based distributed data platforms, ideally involving large-scale data processing and analytics.

Leadership and Management: Proven ability to lead, coach, motivate, and develop teams while establishing clear objectives, managing performance, and driving accountability.

Analytical and Problem-Solving Skills: Strong ability to investigate complex challenges, identify root causes, design practical solutions, and implement process improvements.

Project Management: Excellent organizational and prioritization skills, with the ability to manage competing deadlines, coordinate multiple initiatives, and consistently deliver results.

Communication and Collaboration: Strong written and verbal communication skills, including the ability to document technical findings, present team achievements, and collaborate effectively with international stakeholders.

Personal Attributes: Proactive, adaptable, independent, and solution-oriented, with a strong work ethic, positive attitude, and collaborative mindset.

Global Collaboration: Flexibility to work hours that regularly overlap with teams based in the United States and the United Kingdom.

Additional Advantages: Previous experience with investment management software implementations or Salesforce is a plus.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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