Senior Data / Analytics Engineer, Tines

• Our ideal candidate is someone who has deep experience with SaaS data structures and can both design strategic architecture and implement tactical solutions
• You're excited by the idea of taking Tines from startup mode to a robust, data-driven organization with enterprise-grade data architecture
• You are more interested in solving complex problems than finding complex solutions, and can unblock yourself efficiently when you hit roadblocks or unexpected challenges
• 5-7 years experience in data engineering with significant architectural experience
• Proven track record building data architecture in B2B SaaS, particularly with Salesforce/CRM data
• Deep expertise with modern data stack (Snowflake, DBT, AWS and BI tools - we use Omni)
• Strong background in data modeling and system design
• Experience implementing B2B SaaS metrics and GTM reporting
• Demonstrated ability to translate business requirements into technical solutions
• AWS experience, particularly with data services and S3
• Understanding of data governance and security principles
• Balance of strategic thinking and hands-on implementation skills
• Able to tailor communication (written and oral) appropriately to different audiences, technical/non-technical, of different seniority
• Experience with real-time data processing and streaming

Desirable

• Reporting to our Director of Data and Analytics, we are looking for a Sr. Data / Analytics Engineer to architect and implement our internal data infrastructure
• This is a hybrid role combining hands-on engineering with strategic architectural planning
• You'll work closely with our data team and customer departments to design scalable data systems while ensuring high-quality, consistent, and readily available data across our organization
• You'll be responsible for creating the blueprint for our data infrastructure as we scale, while also building the foundational components
• Design and implement scalable data architecture as Tines grows
• Establish and maintain data modeling standards and best practices
• Build and manage ETL processes across diverse data sources
• Develop core data models in DBT with a focus on efficiency and reusability
• Own our Snowflake implementation and Omni data layer
• Drive data governance and security best practices
• Create clear, maintainable documentation
• Ensure reliable, high-performance data systems
• Partner with stakeholders to translate business needs into technical solutions, including presenting solutions to leadership