• Collaborate with teammates in a disciplined agile environment to solve complex technical problems. • Design and build highly available, scalable services and applications used by internal teams and global business units. • Work across multiple technology stacks, gaining deep exposure to different systems and architectures. • Partner with cross-functional teams to deliver long-term, shared technical and business goals. • Take end-to-end ownership of critical features and infrastructure, from design to production. • Write clean, maintainable, and high-quality code across multiple projects. • Identify, troubleshoot, and resolve bugs, performance issues, and reliability gaps in existing systems.

• Design, build, and maintain scalable data infrastructure to support analytics and reporting across the organization. • Develop and operate ETL pipelines to ingest, transform, and deliver large-scale datasets. • Work with distributed data processing frameworks such as Spark, Hive, or similar MPP architectures. • Use SQL and data modeling techniques to structure and optimize datasets for analytics use cases. • Process and analyze large volumes of structured and semi-structured data using tools such as Spark and Presto. • Write production-quality code using Python, Java, Scala, or Go. • Ensure data reliability and availability, operating and monitoring hundreds of ETL pipelines with strict SLAs. • Investigate and resolve complex data issues, including root-cause analysis of pipeline failures or data inconsistencies. • Partner closely with Data Analysts and cross-functional stakeholders to provide reliable datasets and guide them in using data effectively. • Troubleshoot data issues in dashboarding tools (e.g., Tableau, Power BI, MicroStrategy) and propose solutions.

• Design, build, and maintain scalable data infrastructure to support analytics and reporting across the organization. • Develop and operate ETL pipelines to ingest, transform, and deliver large-scale datasets. • Work with distributed data processing frameworks such as Spark, Hive, or similar MPP architectures. • Use SQL and data modeling techniques to structure and optimize datasets for analytics use cases. • Process and analyze large volumes of structured and semi-structured data using tools such as Spark and Presto. • Write production-quality code using Python, Java, Scala, or Go. • Ensure data reliability and availability, operating and monitoring hundreds of ETL pipelines with strict SLAs. • Investigate and resolve complex data issues, including root-cause analysis of pipeline failures or data inconsistencies. • Partner closely with Data Analysts and cross-functional stakeholders to provide reliable datasets and guide them in using data effectively. • Troubleshoot data issues in dashboarding tools (e.g., Tableau, Power BI, MicroStrategy) and propose solutions.

• Take ownership of software projects from scoping to release. • Work closely with product teammates to plan work; guide conversations about scope and size. • Design efficient, reliable and scalable solutions. • Build software using Elixir, React and associated technologies. • Help define work for and mentor more junior team members. • Give accurate updates on the progress of work. • Lead collaboration with engineering and QA teammates to deliver quality software in a timely manner. • Research and solve complex customer issues. • Keep up-to-date with and lead conversation around industry best practices for design, development, unit testing and other aspects of software engineering.