• Design, build, and maintain scalable data pipelines and ETL processes • Develop and optimize data architectures and data models • Work with large datasets across structured and unstructured sources • Ensure data quality, integrity, and performance optimization • Collaborate with Data Scientists, Analysts, and cross-functional teams • Implement data security and governance standards • Monitor and troubleshoot production data issues • Participate in code reviews and best practices implementation

• Design, build, and maintain scalable data pipelines, streaming infrastructure, and AI/ML data workflows that power data-driven products and enterprise AI solutions • Ensuring reliable, timely, and high-quality data is available across the organization • So that AI Engineers, Product teams, and enterprise clients can make accurate, insight-driven decisions and deliver intelligent customer experiences through Kata's AI and voice platforms

Architect AI-driven workflows and develop custom data applications to enhance stakeholder insights. Serve as the technical lead between data systems and users, ensuring seamless interaction through natural language and automated processes.

• Design, develop and maintain scalable, production-grade data pipelines and build new API integrations to support growing data volume and complexity across our lending and payments products. • Build and maintain modular, tested, and well-documented data transformation models using dbt on top of Snowflake. • Lead high-impact data platform initiatives end-to-end-- from scoping and technical design through delivery, monitoring, and iteration. • Collaborate with analytics, data science, and business teams to evolve data models that power BI tools, regulatory reporting, and data-driven decision making across the organization. • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of sources using SQL, Snowflake, dbt, and AWS big data technologies. • Partner with Executive, Product, Risk, Finance, and Engineering stakeholders to resolve data-related technical issues and support their data infrastructure needs. • Champion data quality, governance, lineage, and cost efficiency across the data platform. • Mentor junior and mid-level data engineers through code reviews, design reviews, and knowledge sharing.