• Work with large volumes of traffic data and user behaviors to develop pipelines that enhance raw data quality. • Break down complex data problems and communicate feasible solutions proficiently. • Extract patterns from large datasets and transform data into actionable insights. • Utilize tools such as Jupyter, SQL, dashboards, statistical analysis, and data visualizations to explore datasets and find answers to business questions. • Partner with internal product and business intelligence teams to identify optimal data ingestion, structure, and storage approaches. • Collaborate with technology field partners to ensure reliable implementation of data solutions. • Provide innovative ideas to improve data and collaborate with engineering, BI teams, and business units to implement changes. • Lead projects from inception to completion, ensuring successful implementation of data solutions. • Contribute to advancing the team in technology and best practices. • Mentor junior team members on best practices and approaches around data.

• Engineer and validate consumer-level attributes from credit bureau and alternative data sources • Apply statistical techniques to uncover insights and improve predictiveness • Monitor attribute performance for stability, compliance, and accuracy • Build dashboards and visualizations (Tableau) to communicate findings • Collaborate with engineering to productionalize prototypes into scalable solutions • Partner with product, risk, and business teams to maximize impact across marketing, risk assessment, and collections • Engage with clients to explain analytics and translate technical results into applicable business insights

• Lead the design and implementation of data ingestion architectures in Snowflake , ensuring scalability and reliability. • Own the development of end-to-end ELT pipelines integrating multiple data sources. • Design and enforce data quality frameworks , including validation rules, testing strategies, and monitoring. • Apply and drive Medallion Architecture (Bronze, Silver, Gold) best practices across data pipelines. • Build and optimize data pipelines and architectures on AWS (S3, Glue, Lambda, EMR, etc.). • Develop and enhance data validation and ingestion frameworks , including UI components (Streamlit-based) when needed. • Act as a technical leader , guiding best practices in data engineering, governance, and pipeline reliability. • Proactively identify risks, bottlenecks, and data quality issues , and implement mitigation strategies before they impact delivery. • Contribute to data governance initiatives , including data definitions, lineage, and stewardship practices. • Drive documentation and continuous improvement of data platform processes.

• Collaborate with data engineers, analysts, and business stakeholders to support data initiatives • Contribute to a positive, inclusive team environment aligned with Innovation’s values • Actively participate in learning, mentoring, and professional development opportunities • Communicate effectively with both technical and non-technical colleagues • Support the delivery of data solutions that enhance member experience and service delivery • Ensure data supports accurate, timely insights that benefit members and internal teams alike • Understand how data products ultimately support member outcomes and organizational goals • Assist in building, maintaining, and optimizing data pipelines and integrations • Support data ingestion, transformation, and validation processes • Contribute to the development of analytics-ready datasets and semantic models • Leverage modern data and cloud platforms to improve efficiency and scalability • Support accurate and reliable reporting through high-quality data practices • Assist in automating data processes to improve efficiency and reduce manual effort • Contribute to analytics and insights that support sound financial and operational decisions • Maintain documentation to ensure sustainability and knowledge sharing • Follow data governance, privacy, and security standards • Support data accuracy, lineage, and consistency across systems • Identify and escalate data quality issues or risks in a timely manner • Contribute to responsible handling of sensitive and confidential information