Data & Analytics Engineering Manager, Loop Returns

• 5 years of hands-on experience as a data engineer or analytics engineer
• 2+ years of experience managing and scaling 3+ person teams
• Expert-level skills in data architecture, modeling, SQL, Python, dbt
• Hands-on ETL experience using tools like Fivetran and Snowpipe for large-scale data integration
• Ability to drive data governance, quality, and observability according to best practices
• Proven project management skills and ability to deliver on timelines
• Excellent communication, leadership, and mentoring skills
• Driven self-starter who can take ownership of delivering data solutions from design to production rollout

Desirable

• We're establishing the conditions to allow anyone to independently answer most of their business questions, and you'll help develop the frameworks and culture that reward the effective utilization of data across the organization while guiding the Data & Analytics Engineering team on strategic projects
• The Data Team is part of Engineering and is responsible for analytics, data platforms, data engineering, and data science/ML
• As part of the Data Team, the Data & Analytics Engineering team collaborates closely with Analytics, providing the data foundation for their analysis, dashboards, and data-driven insights
• By building reliable, scalable data pipelines and optimized data models, this team empowers Analytics to unlock hidden answers in our data
• This strategic partnership enables Analytics to focus on deriving impactful, actionable intelligence
• As Data & Analytics Engineering Manager, you'll directly influence Loop's ability to solve critical merchant challenges and fulfill emerging business needs
• While this high-impact team already does excellent work, you'll champion their excellence across the organization, increasing visibility into their work
• With your technical leadership, we'll build seamlessly scalable data platforms and pipeline architectures that drive informed decision-making
• Cultivate partnerships with data analysts, scientists, product managers, and engineers to understand and meet business data needs
• Lead the team to design, develop, and deploy new data models, pipelines, and products
• Establish and govern SLAs for all production data systems
• Strategize and oversee data warehouse technology, architecture, and content roadmaps
• Take a hands-on approach to analyzing and improving data infrastructure, processes, and performance
• Coach and mentor team members on technologies, techniques, and excellence in data delivery
• Manage and optimize data infrastructure costs, capacity, and performance