• Design, build, and maintain ELT/ETL pipelines that move data reliably from source systems into a cloud data warehouse environment. • Write clean, performant, and well-documented SQL to transform raw data into analyst-ready models and reporting layers. • Develop and maintain Power BI dashboards and reports that give business stakeholders clear, trusted views of company performance. • Apply best practices in data modeling within Power BI (star schemas, DAX measures, performance optimization). • Actively use AI tools (such as LLM assistants, copilots, and agentic workflows) to accelerate development and improve code quality. • Engage directly with business stakeholders to understand data needs in context.

• Data Pipeline Support: Assist in building and maintaining scalable ETL/ELT pipelines using AWS Glue, PySpark, and cloud-native services. • BI Reporting & Visualization: Develop and maintain interactive dashboards and reports using Power BI to visualize cloud cost, performance, and operational metrics for stakeholders. • AI/ML Assistance: Support the development and testing of AI agents and machine learning models designed to monitor cloud resource health. • Data Management: Help manage data storage, processing, and query performance using the Snowflake cloud data platform and AWS Athena. • Workflow Orchestration: Learn to orchestrate and monitor data workflows using Apache Airflow DAGs. • Automation: Assist in automating operational workflows using Python and intelligent scripting. • Collaboration: Participate in code reviews, team design sessions, and Agile ceremonies to learn best practices and contribute to team goals. • Documentation: Maintain clear documentation for data processes and assist in tracking project progress via tools like Jira.

• Work with the corporate Enterprise Data & AI and Consumer Data Teams to own the Consumer division’s curated (“silver” and “gold”) data layers, including harmonization of external market data with corporate sources, in alignment with enterprise lakehouse and governance standards • Drive data “AI-readiness” to help future-proof the data ecosystem, inclusive of metadata curation and documentation of business logic • Develop and operate the Consumer BI ecosystem on the corporate lakehouse platform (e.g., PowerBI, Databricks), enabling scalable self-service by analysts and business users • Enable integration of AI into business reporting and underlying analysis, in order to build early warning systems and to identify underlying business trends worthy of future study • Partner with Consumer division leadership to ensure downstream analysis applications and BI stack are delivering reliable, efficient, and valuable service

• Build and maintain the data infrastructure that supports the company’s and clients’ analytical products, ML models, and GenAI solutions • Design and implement high-scale batch and real-time data ingestion and transformation pipelines • Build and maintain lakehouse architectures using AWS S3, Glue, Redshift, and Apache Iceberg • Develop and orchestrate ML/AI pipelines using AWS SageMaker and Apache Airflow • Implement real-time streaming solutions with Apache Kafka and/or AWS Kinesis • Explore and apply GenAI patterns via AWS Bedrock, including RAG pipelines, embedding workflows, and integration with LLMs • Apply Data Mesh practices to decentralize data domains and improve team autonomy • Ensure data quality, lineage, and governance using dbt and AWS Glue Data Catalog • Optimize cost and query performance in Redshift and Athena environments