• Develop the Analytics layer in our data warehouse using dbt code. • Define complex business logic using SQL. • Collaborate with business partners to understand their data needs. • Work closely with both the data engineering team to ensure scalability.

• 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

• Work to make Wave’s data easy, safe, and reliable to use, enabling better products for our customers. • Leading dimensional design to structure Wave’s analytics data. • 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