• Design and implement understandable data models that support flexible querying and data visualization • Instrument algorithms and machine learning pipelines from complex business requirements • Advance automation efforts that help the team spend less time manipulating & validating data and more time analyzing it • Guide the Analytics Engineering roadmap, communicate timelines, and manage development cycles/sprints to deliver value • Own the creation and support of internal data architecture and governance standards and best practices • Lead the selection, implementation, optimization, and integration of data tools • Rapidly deliver on concepts through prototypes that can be presented for feedback • Train fellow employees on best practices for data standards, DAGs, code, documentation and visualization and help others act as successful stewards of our internal tools

• Leading dimensional design to structure Wave’s analytics data in ways that are helpful for human and AI end users. • Managing our extensive snowflake estate with a strong analytical mindset. • Building tools to improve the analytics development experience. • Responsible for the design, build and health of all of our ETL pipelines.

• Create and iterate on data products and develop pipelines that can be utilized to provide this data on an on-going basis. • Assist in enhancing data delivery across PL and Distribution. • Assist with pivoting from antiquated technologies to enterprise standards. • Responsible to understand, analyze & translate business data stories into a technical stories' breakdown structure. • Design, build, test and implement data products of varying complexity, with limited coaching and guidance. • **Design, build, and maintain ETL/ELT pipelines using Python and SQL** • Develop data validation scripts in Python • Write SQL queries to detect anomalies, duplicates, and missing values • Work closely with data analysts, scientists, and business stakeholders

• Build foundational analytical constructs in Python • Develop reusable Python modules that transform structured datasets into analytical building blocks used across the platform • Create structured analytical outputs that AI systems can reliably interpret • Develop analytical tools that allow the platform’s conversational interface to answer data questions reliably • Collaborate with data platform teams to consume structured datasets from environments such as Databricks • Implement validation logic to ensure analytical outputs reconcile with underlying datasets • Design analytical outputs that maintain clear traceability between source data, analytical computations, and AI-generated explanations