• Lead the design and evolution of the client's lakehouse/data lake architecture • Provide technical direction for the design and deployment of AI agents aimed at enabling the sales team to rapidly access fund performance insights • Define and drive the roadmap for Large Language Model adoption and Generative AI integration within the client's data ecosystem • Act as the primary technical point of contact for client leadership and senior business stakeholders • Implement and enforce DataOps principles across the data platform • Mentor and develop team members, fostering a culture of continuous learning

• Design, develop and maintain streaming data pipelines and transformations • Build and operate a big data platform used for real-time network observability and alerting • Ensure platform availability, reliability and performance • Design and implement monitoring solutions for data pipelines and platform components • Support platform evolution, upgrades and infrastructure improvements • Collaborate with internal stakeholders and translate business requirements into technical solutions • Troubleshoot complex platform and data processing issues • Create and maintain technical documentation and operational standards

• Lead, mentor, and develop a high-performing DataOps team • Set technical direction, priorities, and operating standards for DataOps • Own the operational health, performance, and availability of CloudPay’s data platforms • Design and implement monitoring, alerting, and incident management for data pipelines • Implement robust CI/CD processes for data pipelines and analytics assets • Ensure strong data access controls, auditability, and regulatory compliance (e.g., GDPR) • Act as a bridge between Data Engineering, Analytics, Product, and Operations

• Assist in identifying and resolving QA bugs related to data pipelines and processes • Validate data accuracy, completeness, and consistency • Participate in designing, building, and maintaining data pipelines • Implement data transformations, data cleansing, and ETL processes • Collaborate with senior engineers to enhance pipeline efficiency • Take ownership of small features within the data pipeline • Write code to handle data ingestion, processing, and storage • Lead a small independent project related to data engineering • Choose a topic of interest (e.g., optimizing pipeline performance, automating data validation) • Present your findings and recommendations to the team