• Connect with cross-functional teams: The data environment we’re building will be used by members of the Accounting, Finance, Supply Chain, and Publishing teams, among others. • Project Management: Keep track of & prioritize short-term and long-term projects. • Build & Support: Build new and improved ways for us to access & view data, and see to their steady functioning over time. • Investigate & Iterate: Keep an eye on how small improvements can be made to improve the cost and reliability of our data pipeline.

• Design, build, and maintain scalable data pipelines and workflows across modern cloud data platforms—Snowflake, Databricks, Microsoft Fabric, or equivalent • Implement ELT/ETL processes with a focus on data quality, performance, reliability, and maintainability • Assemble and transform large, complex datasets that meet both functional and non-functional business requirements • Build and optimize data models to support analytics, reporting, and AI/ML use cases • Work across cloud environments (AWS, Azure, GCP) and their native data services • Contribute to solution design discussions alongside architects; bring engineering-level perspective on feasibility, complexity, and implementation trade-offs • Help define data pipeline patterns, platform configurations, and engineering standards within the engagement • Identify opportunities to improve data infrastructure: automate manual processes, improve data delivery, redesign for greater scalability and performance • Build analytics tools and data products that surface actionable insights for clients across key business metrics • Support integration with BI and visualization tools (Power BI, Tableau, Looker, Qlik, or similar) • Ensure data products are well-documented, governed, and ready for downstream consumption • Participate in client discovery and requirements-gathering sessions; contribute an engineering-level perspective on feasibility, complexity, and implementation approach • Support pre-sales and scoping activities alongside Architects and Pre-Sales teams—help validate that proposed solutions are technically achievable before commitments are made • Engage directly with client technical teams throughout the engagement lifecycle; build credibility through engineering quality and clear communication • Work effectively across multiple client engagements at different stages of the implementation lifecycle • Collaborate with architects, solution owners, and client technical teams to deliver against agreed outcomes • Mentor junior data engineers; share knowledge and raise the engineering quality of the teams you work with • Communicate technical progress, blockers, and decisions clearly to both technical and non-technical stakeholders.

• Develop technical enablement content, including presentations, solution guides, white papers, workshops, labs, and training materials. • Design, build, and maintain product demonstrations that showcase customer challenges, technical capabilities, and business outcomes. • Support field sales, systems engineering, and channel teams by providing technical expertise and creating customer-facing content. • Deliver presentations at customer meetings, internal enablement sessions, partner events, and industry conferences. • Collaborate closely with Product Management, Engineering, Marketing, and regional field organizations to support product launches and strategic initiatives. • Stay current on emerging technologies and industry trends in data center networking, automation, AI, and cloud infrastructure. • Translate complex technical concepts into clear, compelling messaging for technical and executive audiences.

• Implement advanced data engineering solutions • Use agile methodologies and industry best practices • Plan and execute data integration strategies • Ensure data quality and security • Develop and implement ETL processes, creating documentation • Create management reports using Power BI