• Design, develop, and maintain scalable data pipelines and transformations • Build and optimize complex SQL queries and data models • Implement and manage real-time and batch data ingestion workflows using CDC • Orchestrate end-to-end data pipelines with monitoring and alerting • Build reports and dashboards in BI tools to support business analytics • Collaborate with analytics and engineering teams to deliver reliable data solutions • Ensure data quality, consistency, and reconciliation across the pipeline • Monitor and optimize data platform performance and costs • Ensure data quality, reliability, and governance standards

• Develop and deliver data pipelines and transformations in line with existing design specifications and delivery standards. • Build and maintain data products within Snowflake, using dbt and ADF to orchestrate and transform data. • Implement robust testing and validation to ensure data quality, consistency, and accuracy. • Follow established development patterns and governance standards, ensuring alignment with data delivery framework. • Manage deployment and documentation of developed components, ensuring solutions are well-structured and maintainable. • Support troubleshooting and issue resolution during development and delivery cycles.

• Design, build, and maintain scalable ELT/ETL pipelines focused on cloud cost attribution and financial transparency • Develop data models that integrate infrastructure telemetry with cost datasets to support performance-aware financial analysis • Build automation frameworks that identify optimization opportunities, including Reserved Instances and Savings Plans strategies • Enable data-driven decision-making by delivering high-quality datasets to Product, Engineering, and Finance stakeholders • Own schema design and evolution for a Cloud Economics data warehouse, ensuring scalability and data integrity • Perform end-to-end data validation and auditing to guarantee accuracy of financial reporting at an executive level

• Data architecture and engineering • Design and implement scalable, reliable data pipelines • Structure data lakes / data warehouses to support CRM use cases • Ensure data performance, governance and quality • Integrate data from multiple sources (Salesforce, Marketing Cloud, internal systems, digital channels) • Structure customer data (profile, behavior, interactions, transactions) • Ensure consistency and unification of the 360° view of the customer • Make data available for segmentation and activation in CRM tools • Build and maintain data extensions / tables for campaigns • Support automated flows with up-to-date, reliable data • Prepare data for propensity, uplift, churn and LTV models • Operationalize consumption of model outputs (scores, segments, next best action) • Ensure recurring updates and integration with activation systems • Implement validations, monitoring and quality alerts • Document pipelines and data structures • Ensure compliance with data and privacy policies • Collaborate with data scientists, CRM analysts and business teams • Translate business needs into scalable technical solutions • Support the evolution of the client's data architecture