Mammoth Growth is a modern data analytics consultancy. We use our unique combination of agnostic tools expertise and data-driven decision making to help our clients grow and thrive in an ever-changing marketplace. We are world-class business consultants who optimize systems to deliver measurable business outcomes. We have completed 850+ engagements with clients in all areas - e-commerce, SaaS, media, D2C, B2B - from start-ups to enterprise.  Mammoth is trusted by industry leaders from Dropbox to Calendly to verify their data, manage their reporting infrastructure, develop growth strategy and provide recommendations and insights that achieve measurable results. At Mammoth Growth, the Senior Analytics Engineer / Jr. Tech Lead focuses on building complex data infrastructure using dbt, Snowflake, and modern ELT tools while providing technical guidance and expertise. You'll design scalable data pipelines, implement governance frameworks, and leverage AI tools to optimize development workflows. The position requires hands-on technical work combined with code review responsibilities and team guidance. What You'll Do Build complex data models using dbt with optimal performance and maintainability Design and optimize Snowflake data pipelines leveraging advanced features for scalability Configure database environments, manage permissions, access control, and implement data governance frameworks Implement ELT processes using modern data stack tools such as Fivetran Write high-quality code following Git workflows, code reviews, and CI/CD practices Conduct data quality audits and implement automated testing frameworks Leverage AI tools (Claude, ChatGPT) to enhance development productivity and solution quality Apply prompt engineering and implement agentic workflows using MCPs Mentor Analytics Engineers I and II, providing technical guidance and code reviews Collaborate with Technical Leads and Sr. Consultants on complex project decomposition and execution Present technical solutions and audit findings to clients Independently manage project components while coordinating with cross-functional teams Support project planning with technical expertise and risk assessment What You'll Bring

Meet Upside: We created Upside to transform brick-and-mortar commerce. Our technology uses the sophistication of online retail—profit measurement, attribution, and incrementality—to provide users with more value on their everyday purchases and brick-and-mortar businesses with new, profitable customers. We’ve helped millions of users earn 2 to 3 times more cashback than any other product, and hundreds of thousands of brick-and-mortar businesses earn measurable profit. Billions of dollars in commerce run through the Upside platform every year, and that value goes directly back to our retailer partners, the consumers they serve, and important sustainability initiatives. About the role: We're looking for an Analytics Engineer to join the team responsible for Upside’s core data platform. You’ll play a key role in building trusted data products that serve both internal teams and customer-facing features, while also improving the analytics platform and enabling others to do their best data work. This is a great opportunity for someone who’s confident working with large datasets and complex business logic, thrives in a fast-moving environment, and wants to grow as a technical contributor while collaborating closely with product managers, analysts, data scientists, and engineers across the company. Here are some ways we have seen analytics engineers drive impact at Upside: Design and build complex, scalable data products that support internal analytics and power product features, using tools like dbt, Snowflake, and Dagster. Translate ambiguous business needs into structured, trustworthy data assets—owning development end-to-end, from design through deployment and monitoring. Contribute to shared platform tooling that improves pipeline orchestration, testing, access control, and observability for both engineers and analysts. Evaluate and implement new platform capabilities , such as Semantic Views, Cortex, or SnowML, to improve performance, maintainability, and user experience. Support and enable teammates across the org , including onboarding new users, resolving data issues, and documenting patterns and best practices. Maintain reliability and data quality through shield support rotations, strong monitoring, alerting, and test coverage. Why You Should Apply This role is a good fit for you if: You aren’t afraid to challenge the status quo when it makes the team and business better. You learn from those around you while utilizing data to advocate for informed change. You thrive at the intersection of systems and storytelling, not only building robust solutions but also communicating their purpose, impact and rationale, so teams can experiment, iterate, and act confidently. You care about building resilient systems that scale. You bring a mindset of continuous improvement, and know when to invest in observability, automation, or new infrastructure to reduce toil and improve outcomes for the team and end users. You believe that pulling quality upstream starts with engineering. You champion best practices, encourage early testing and validation, and work closely with peers to build a culture of quality from the ground up. Ideal Qualifications Preferred Qualifications Experience supporting machine learning workflows, such as building features or monitoring model inputs and outputs. Familiarity with DevOps practices (e.g., CI/CD for data), data governance, or FinOps (cost-conscious design). Experience working in a fast-growing startup environment or on platform-style teams that serve internal customers. Engineering Culture: We want our engineers to have the time and support to grow in their craft and contribute meaningfully to impactful technical decisions. Engineers are encouraged to focus deeply on their work, collaborate effectively with team members, and continuously develop their skills. Teams are thoughtfully staffed to create a dynamic and diverse environment that enhances learning and innovation. Location: This hybrid role is based in our Austin, Chicago, DC, or NYC office. In-office attendance is required on Monday, Tuesday, and Thursday and may increase based on project-based needs and changes to Upside’s in-office policy over time. Compensation: The US base salary range for this full-time position is $130,000 - $180,000 + equity + benefits. The final starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. Your recruiter can share more about the specific salary range during the hiring process. #LI-Hybrid #LI-NE1 Benefits: Medical, dental, and vision coverage starting on Day 1 Equity (ISOs) 401(k) program Family planning programs + paid parental leave Physical fitness and wellness memberships Emotional and mental health support programs Unlimited PTO + 10 paid federal holidays + our annual, week-long Winter Break Flexible work environment Lunch reimbursement for in-office employees Employee Resource Groups Learning and Development stipend Transparent culture Amazing mission! Diversity and Inclusion: Diversity drives innovation, and our differences make us stronger. We‘re passionate about building a workplace that represents a variety of backgrounds, skills, and perspectives, and we do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Everyone is welcome here! If there's anything we can do to support a disability or special need during your application or interview process, please email accommodations@upside.com. This email is for accessibility accommodations only, it should not be used to submit job applications. Notice To Recruiters And Placement Agencies: This is an in-house search with a dedicated recruiter. Please do not submit resumes to any person or email address at Upside. Upside is not liable for, and will not pay, placement fees for candidates submitted by any party or agency other than its approved recruitment partners.

• Design, develop, and maintain data models (e.g., in dbt) that align with the overall data architecture and business requirements. • Manage data pipelines end-to-end, from data extraction to transformation and loading, to provide timely and correct data for analysis. • Utilize complex SQL queries and macros to transform data efficiently, optimizing queries for performance as needed. • Incorporate data quality checks and cleansing routines to ensure datasets are reliable and accurate. • Work closely with data analysts and other analytics engineers to ensure consistency and accuracy in metrics and definitions across the team. • Contribute to team processes by occasionally leading portions of meetings or sharing improvements to analytics workflows.

• Design, develop, and maintain scalable ETL/ELT pipelines that support cross-functional data needs • Build, deploy, and optimize Power BI dashboards and other reporting tools to support strategic decision-making • Write well-structured, testable code in Python, R, and SQL to support analytics workflows • Lead the development and enforcement of version control, documentation, and coding standards • Partner with data scientists to bring analytics products from prototype to production • Identify and integrate AI-driven tools to enhance data processing and reporting capabilities • Mentor junior team members and contribute to a culture of knowledge sharing and continuous improvement