Data Analyst

Company Description

Vested’s mission is to enable sustainable wealth creation by providing cross-border investment opportunities for Indians across the world. We serve resident Indian and NRI investors, helping them access investment opportunities in global and Indian markets and diversify their portfolios.

Our journey began with US stocks and ETFs and has expanded to include Global Funds, Private Markets and GIFT City investment opportunities. With $1.5 billion in assets under administration (AUA), Vested is preparing for its next phase of growth: becoming a truly global investing platform, with plans to extend access to more than 20 markets beyond the US.

Our culture is guided by six values: Prioritize users, Own the problem, Be transparent, Make data-informed decisions, Grow together and Strive for excellence. We start with customer needs, work to earn their trust, take responsibility for outcomes and communicate openly. We encourage thoughtful disagreement, continuous learning and high standards of work.

For this role, those values mean understanding investors, choosing meaningful metrics, being honest about uncertainty and building reliable analytics.

Role Description

We’re looking for an Analyst / Senior Analyst – Business & Data Analytics with 4–6 years of relevant experience, based in GIFT City, Gandhinagar, Gujarat.

This role combines business and product analytics with data modelling, practical data engineering and reporting automation. You’ll own analytical problems and help build reusable master tables, reliable reporting datasets and automated workflows that scale with Vested.

Working with Product, Business, Marketing, Finance, Engineering and Leadership, you’ll turn insights into action. Senior Analysts will also lead workstreams, mentor colleagues and own delivery quality.

Key Responsibilities

Business and Product Analytics

• Own analysis end-to-end: frame the question, choose the approach, validate data, recommend actions and track outcomes.
• Define meaningful KPIs for existing and new products, connecting customer behaviour with product adoption, assets under management (AUM), net deposits, trading activity, revenue and retention.
• Use funnel, cohort, segmentation and retention analysis to identify customer drop-offs and improve conversion and engagement.
• Investigate performance changes across customers, products, campaigns and market conditions, making assumptions and limitations clear.
• Evaluate launches, campaigns and experiments using clear success metrics and appropriate comparisons, translating findings into practical improvements.
• Apply financial-product knowledge when interpreting results—for example, distinguishing market-driven changes in AUM from changes caused by customer deposits or withdrawals.Data Modelling and Analytics Foundations
• Design and maintain reusable master tables and reporting datasets across customers, accounts, transactions, holdings and products to support recurring analysis and business reporting.
• Define consistent data structures, table relationships, identifiers and levels of detail so teams can combine datasets accurately and avoid double counting.
• Partner with Engineering to improve how data is captured, transformed and made available for analytics. Translate reporting requirements into practical data-model and pipeline improvements.
• Establish shared metric definitions and document calculation logic, source systems and dependencies so business teams can understand and trust the numbers.
• Maintain historical tracking and reliable refresh processes so reporting is consistent and source-data changes can be investigated.
• Build data-quality checks for accuracy, completeness, duplicates and freshness. Identify discrepancies early and work with relevant teams to resolve their causes.
• Design datasets for maintainability, query performance, new products and growing data volumes.Reporting, Dashboards and Automation
• Develop clear, useful dashboards and recurring reports that help teams monitor performance, identify exceptions and make decisions.
• Automate repeatable data preparation, reporting, dashboard refreshes and quality checks using SQL, Python or suitable workflow tools.
• Build dependable workflows with appropriate scheduling, validation and failure visibility, reducing manual effort and the risk of inconsistent outputs.Stakeholder Management and Team Leadership
• Translate stakeholder requests into clear requirements, priorities and deliverables, aligned with the decisions they need to support.
• Explain findings and trade-offs in clear business language, recommend next steps and communicate progress and blockers early.
• Prioritise work based on business impact, urgency and effort, balancing immediate analytical requests with improvements to the long-term data foundation.
• For Senior Analysts: independently lead complex projects, coordinate workstreams and take responsibility for timelines and delivery quality.
• For Senior Analysts: mentor junior analysts, review SQL, analytical approaches and data models, and help the team develop consistent working practices.Qualifications
• 4–6 years of relevant experience in business analytics, product analytics, data analytics or a closely related role, with demonstrated ownership of analytical projects.
• Strong SQL skills across joins, aggregations, window functions and complex datasets, including validating results and identifying inefficient or incorrect logic.
• Strong fundamentals in statistics, validation, funnel and cohort analysis, segmentation, retention and experiment evaluation.
• Practical exposure to data engineering or data architecture, including relational databases, data modelling, warehouse concepts, ETL/ELT pipelines and reusable analytics tables.
• Hands-on experience with Tableau, Power BI, Metabase or similar tools, and choosing effective visualisations.
• Experience automating reporting or data workflows. Python skills for data preparation, validation and repeatable analysis are valuable.
• Understanding of financial markets, investment products and business economics. Experience in fintech, financial services, brokerage, wealth management or investment platforms is preferred.
• Personal investing experience is highly valued. Use your understanding of portfolio allocation, returns, deposits, withdrawals and investor behaviour to ask better analytical questions.
• Clear communication and stakeholder-management skills, including explaining complex findings to non-technical audiences.
• For Senior Analyst: experience mentoring analysts or managing small teams, leading analytical workstreams and reviewing the quality of others’ work.What We Are Looking For
• Prioritize users: Start with investor needs and work backwards. Look beyond activity metrics to understand whether products and decisions create value for customers and strengthen their trust.
• Own the problem: Keep asking why until you understand the root issue. Take initiative, follow through on actions and own the outcome. Be comfortable admitting what you do not know and seeking the right input.
• Be transparent: Communicate assumptions, limitations, mistakes and blockers openly. Make your work understandable and visible, and raise concerns early, even when the conversation is uncomfortable.
• Make data-informed decisions: Use meaningful evidence to guide recommendations and evaluate results. Question misleading metrics, distinguish correlation from causation and combine analysis with business context.
• Grow together: Share knowledge, help colleagues develop and respectfully challenge ideas. Welcome different perspectives and constructive feedback, then commit to agreed decisions. Senior Analysts should actively coach others.
• Strive for excellence: Be thorough in analysis, validation and documentation. Consider how others will use your work, build maintainable solutions and learn from well-considered experiments that do not succeed.

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