Technical Business Analyst

About the Role

This office-based position in Stamford, CT, supports global Investment Technology initiatives, primarily within the Alternative business. It requires regular in-person collaboration with business and technology partners.

Key Responsibilities

• Gather, analyze, and document business requirements from investment, operations, product, and technology stakeholders.
• Translate requirements into user stories, process flows, data mappings, business rules, and acceptance criteria.
• Perform data analysis, validations, reconciliations, and root-cause analysis to support business initiatives and issue resolution.
• Support data-quality activities, including exception management, remediation, and control checks.
• Assist with the design and delivery of reports, dashboards, and analytics using SQL, Python, Power BI, Tableau, or similar tools.
• Identify opportunities to apply AI tools and AI agents to automate repetitive tasks, accelerate analysis, and improve documentation and reporting.
• Help design, test, and document AI-assisted and agent-based workflows, ensuring outputs are validated, controlled, and aligned with firm AI governance.
• Participate in testing, release validation, user acceptance support, and post-production support activities.
• Build relationships with business users, technology teams, and vendors; provide clear status updates and follow through on commitments.
• Support platform enhancements, upgrades, issue resolution, onboarding, and user adoption.

Requirements & Tech Stack

• AI Mindset and Utilization
• AI-forward mindset with a genuine interest in how generative AI and agentic AI can transform business and technology processes.
• Hands-on experience using AI tools (e.g., Microsoft 365 Copilot, ChatGPT, GitHub Copilot, or similar) for analysis, documentation, coding, and productivity.
• Understanding of AI agents and agentic workflows.
• Ability to write effective prompts, critically evaluate AI outputs for accuracy, and apply human review before use.
• Awareness of responsible AI principles, data privacy, security, and model risk considerations in a regulated financial services environment.
• Curiosity and learning agility to keep pace with rapidly evolving AI capabilities and apply them to real business problems.
• Business Analysis and Stakeholder Management
• Foundational experience or strong aptitude in requirements gathering, process analysis, and documentation.
• Excellent written and verbal communication skills, with the ability to explain technical topics clearly to business users.
• Ability to build working relationships with stakeholders across investment, operations, technology, and vendor teams.
• Ability to manage multiple priorities, take ownership, and ask the right questions.
• Data Analysis Competencies
• Strong analytical and problem-solving skills with attention to detail.
• Ability to work with large datasets to identify trends, anomalies, and data-quality issues.
• Exposure to data validation, reconciliations, and root-cause analysis.
• Technical Skills
• Working knowledge of SQL, including queries, joins, and basic data manipulation.
• Programming knowledge in at least one language is required. Python is strongly preferred for data analysis, automation, and reporting.
• Understanding of relational databases, data models, and data warehouse concepts.
• Familiarity with ETL concepts, APIs, and file-based integrations is a plus.
• Exposure to reporting and dashboard tools such as Power BI, Tableau, or similar technologies.
• Domain and Platform Knowledge
• Primary domain focus: Alternative Investments (Private Equity, Private Credit, Real Estate, Infrastructure, Real Assets, Hedge Funds, Fund-of-Funds).
• Interest in or basic knowledge of investment operations, portfolio accounting, performance measurement, and investment reporting is preferred.
• Exposure to eFront products (FrontInvest, Insight) or platforms like Burgiss, Argus, Dynamo, PFaroe, Preqin, or Aladdin is an advantage. Training will be provided.

Qualifications

• A bachelor's degree is required. A degree in Computer Science, Information Systems, Information Technology, Engineering, Mathematics, Finance, or Economics is preferred.
• 0–3 years of relevant experience in business analysis, data analysis, technology, or financial services; internships and project experience will be considered.
• Coursework, certifications, or personal projects in AI, machine learning, data analytics, or automation are a strong plus.
• Interest in financial services and alternative investments.