• Partner directly with clients to understand their business needs, data challenges, and desired outcomes • Design, build, and maintain production‑grade data models that support client‑specific reporting, analytics, and operational workflows • Develop and maintain dbt models, tests, documentation, and transformation logic as part of broader client solutions • Develop, review and test code (SQL, Python, BI tool like Tableau and PowerBI) • Collaborate with data engineers to ensure upstream data is structured and optimized for downstream use • Translate client requirements into scalable, well‑documented semantic layers • Implement data quality checks, testing frameworks, and governance standards across client projects • Participate in client sessions, including solution design conversations • Contribute to internal best practices, reusable patterns, and accelerators for future client engagements • Report work status to team leaders and participate actively in daily standups • Additional responsibilities as assigned

• Perform advanced data analysis using statistical and analytical techniques • Develop, deploy, and interpret predictive and AI/ML models • Build and apply dynamic process models for industrial systems • Translate complex data insights into actionable recommendations • Collaborate with cross-functional teams and present findings effectively • Support on-site installation and commissioning activities as required

• Build and maintain robust data models using dbt, ensuring data accuracy and accessibility across the organization through automated processes and improved data quality checks • Design and migrate dashboards and reports to modern BI tools like Count, translating complex data requirements into clear visualizations that support merchant and issuer success. • Develop analytical frameworks for merchant segmentation, performance metrics, and campaign measurement, providing insights that drive strategic decisions and optimize partner relationships. • Partner with sales, account management, and product teams to understand data needs, scope technical solutions, and deliver analytics that directly support revenue growth and operational efficiency. • Lead pitch development and technical investigations, identifying opportunities to improve data workflows, reduce costs, and enhance the scalability of analytics infrastructure.

• Design and implement a formal analytics data layer using AWS Glue, Presto, and LookML • Collaborate within the Data Science & Analytics team and across Product & Engineering to define, document, and maintain alignment on metric definition and data lineage • Develop and maintain automated data reconciliation and quality checks to proactively identify and resolve discrepancies, ensuring accuracy and consistency of critical reports and dashboards • Lead investigations into complex data anomalies, conduct root cause analysis, and communicate findings and solutions effectively to both technical and non-technical audiences • Mentor and guide members of the data science and analytics team, establishing and enforcing best practices around data modeling, testing, documentation, and code review