We are looking for a data-driven and analytical Data Analyst to join our growing Analytics team. In this role, you will transform complex business data into meaningful insights that drive strategic decisions across Product, Risk, Operations, and Business teams. You will work with large datasets, build dashboards, automate reporting, and identify trends that improve business performance and operational efficiency.
The ideal candidate has strong SQL skills, hands-on experience with Snowflake and BI tools, and is passionate about solving business problems through data.
What we expect you to perform:
- Collect, analyze, and interpret large volumes of structured and unstructured data to identify trends, patterns, and business opportunities.
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Translate business questions into analytical solutions and actionable recommendations.
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Perform exploratory data analysis (EDA) to uncover insights that support strategic decision-making.
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Present analytical findings through clear visualizations and executive-friendly reports.
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Design, build, and maintain interactive dashboards using Tableau (or similar BI platforms).
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Develop automated reports to monitor business KPIs, operational metrics, and performance trends.
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Ensure reporting accuracy, consistency, and timely delivery across stakeholders.
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Continuously improve reporting processes through automation.
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Write complex SQL queries to extract, transform, and validate data from Snowflake and relational databases.
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Develop reusable datasets and reporting layers for business users.
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Ensure high-quality, reliable, and well-documented data assets.
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Work with engineering teams to improve data availability and accessibility.
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Analyze customer behaviour, operational performance, product usage, and business metrics.
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Support Product, Risk, Finance, and Operations teams with ad-hoc analysis.
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Identify key business drivers influencing growth, retention, productivity, and profitability.
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Recommend process improvements based on analytical findings.
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Monitor data quality, completeness, and consistency across reporting systems.
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Identify anomalies, inconsistencies, and root causes of data issues.
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Collaborate with Engineering teams to resolve data discrepancies.
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Support data governance and documentation initiatives.
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Automate recurring reports and manual analytical processes using SQL and Python.
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Improve reporting efficiency by developing scalable analytical solutions.
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Support data pipeline validation and analytical workflow optimisation.
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Contribute to continuous improvement initiatives across the analytics function.
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Partner with Product, Engineering, Finance, Operations, and Business teams to understand analytical requirements.
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Communicate technical findings to non-technical stakeholders in a simple and meaningful manner.
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Participate in requirement gathering, solution design, and business review meetings.
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Support data-driven decision-making across the organization.
What we are looking for:
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3–5 years of experience as a Data Analyst, or similar analytical role.
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Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Economics, Engineering, Information Systems, or a related quantitative discipline.
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Strong analytical thinking with excellent problem-solving skills.
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Experience working with large datasets and business reporting.
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Ability to convert business requirements into analytical solutions.
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Strong stakeholder management and communication skills.
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Experience working in fintech, banking, lending, financial services, or technology companies is preferred.
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Strong SQL skills with experience writing complex joins, CTEs, window functions, stored procedures, and performance optimization.
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Hands-on experience with Snowflake or modern cloud data warehouses.
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Python for data analysis and automation (Pandas, NumPy, Matplotlib, etc.).
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Experience developing reusable analytical scripts and automating workflows.
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Strong understanding of data modelling, KPI development, business metrics, and statistical analysis.
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Experience with trend analysis, forecasting, segmentation, and root cause analysis.
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Knowledge of A/B testing and experimentation is an advantage.
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Experience working with ETL processes and data transformation.
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Understanding of data quality, governance, and validation principles.
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Exposure to cloud platforms (AWS preferred) is a plus.
Preferred Skills
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Experience in fintech, digital lending, banking, payments, or financial services.
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Knowledge of customer lifecycle analytics, operational analytics, or business intelligence.
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Familiarity with Git, Jira, and Agile development methodologies.
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Basic understanding of machine learning concepts and predictive analytics is an added advantage.
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Experience working with APIs and modern data ecosystems is desirable.
