• Contribute to the development and enhancement of internal tools and applications using technologies such as Cloverleaf, Azure DevOps, and data integration frameworks. • Implement and optimize data integration workflows to ensure seamless connectivity between systems and applications. • Participate in Agile ceremonies and collaborate with cross-functional teams to deliver high-quality software. • Assist in debugging, testing, and documenting code/routes. • Explore and apply modern engineering practices including CI/CD, cloud-native development, and data integration strategies. • Engage in team events, lunch & learns, and training activities throughout the program.

• Design, develop, and maintain scalable data pipelines. • Build and optimize data infrastructure. • Implement data quality monitoring and validation frameworks. • Optimize data storage, processing, and query performance for large-scale datasets. • Design and implement data models for analytics and reporting use cases. • Develop tools and automation to improve data engineering workflows and productivity. • Ensure data governance, security, and compliance standards are met. • Participate in on-call rotation to support production data systems.

• Design, develop, and maintain ETL/ELT pipelines on cloud-based data platforms • Build data ingestion, transformation, and orchestration workflows using tools such as Azure Data Factory, Airflow, Fivetran, or similar • Develop transformations and data processing logic using platforms such as Databricks, Snowflake, or equivalent • Work with structured and semi-structured data from multiple source systems (APIs, databases, files) • Collaborate with analytics, BI, and data science teams to deliver curated, analytics-ready datasets • Implement data quality checks, logging, monitoring, and error handling • Participate in technical design discussions, effort estimates, and sprint planning • Produce clear and maintainable technical documentation

• Design, code, test, debug and document programs and scripts using agreed standards and tools, ensuring well-structured deliverables. • Ensure data quality and implement tools and frameworks to automate the detection of data quality issues. • Profile data sources and develop ETL processes, with knowledge of data modeling fundamentals, using SQL, Python and ETL/ELT support tools. • Assist management in preparing estimates and proposals for clients. • Plan effective solutions for data storage, security, sharing and publication within the organization. • Create and update documentation of process flows and business rules. • Maintain existing pipelines and process large volumes of data as needed.