• Work on strategic projects involving migration, integration, automation, and process improvement • Design, implement, and maintain data pipelines connecting OMS/EMS, risk/performance systems, internal platforms, custodians, and administrators • Build data pipelines capable of identifying business opportunities from internal and external data • Build and maintain high-reliability, high-performance ETL/ELT processes • Develop integrations using APIs, SFTP, messaging systems, or proprietary connectors • Document processes, data flows, and technical solutions for future reference, ensuring knowledge is shared and preserved • Collaborate with business analysts to define calculation rules, granularity, data quality standards, and key fields • Identify and implement continuous improvements focused on efficiency, performance, governance, and cost, delivering business value

• **Build** scalable, resilient, high-performance data pipelines on Databricks and AWS. • **Develop** solutions in Python and SQL, applying best practices, standards, and automation. • **Implement** data models and processing layers based on Lakehouse architecture. • **Optimize** Spark jobs, ensuring efficiency, governance, and reliability of workloads. • **Integrate** different data sources, ensuring security, quality, and compliance. • **Collaborate** with architects and engineers to define technical solutions, standards, and engineering practices. • **Support** platform user squads by providing technical guidance and adoption support. • **Monitor** pipelines, metrics, and performance, proactively addressing issues. • **Document** solutions, flows, processes, and engineering standards. • **Contribute** to modernization, automation, and continuous improvement initiatives for the Data & AI Platform.

• Build scalable and reliable data pipelines that collect, transform, load and curate data from internal systems • Augment data platform with data pipelines from external systems. • Ensure high data quality for pipelines you build and make them auditable • Drive data systems to be as near real-time as possible • Support design and deployment of distributed data store that will be central source of truth across the organization • Build data connections to company's internal IT systems • Develop, customize, configure self service tools that help our data consumers to extract and analyze data from our massive internal data store • Evaluate new technologies and build prototypes for continuous improvements in data engineering.

• Design, develop, and implement data pipelines, data integration, and data storage solutions • Partner closely with IT Managers, Enterprise Business Intelligence, Data Governance, and Data Science teams • Lead the design and build of scalable data pipelines and solutions (batch and/or streaming) • Analyze complex data elements and systems, data flow and develop conceptual, logical, and physical data models • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources • Collaborate in implementing components of data strategy – Master Data Management (MDM), data virtualization etc.