About the role
MX is a fintech company on a mission to empower the world to be financially strong. We build technology that helps banks, credit unions, and fintechs deliver smarter, more intuitive financial experiences to millions of people. Like many startups, we’ve navigated real growth challenges — and we’ve come out stronger on the other side. Today, MX is in a phase of renewed momentum and scale, with a solid foundation and a clear vision for what’s next. This is a place where thoughtful execution matters, innovation is encouraged, and individuals have real ownership over their work. Our culture values curiosity, accountability, and impact. We give people the space to question assumptions, design better solutions, and help shape how the company grows. If you’re looking to do meaningful work, influence outcomes, and grow alongside a company that’s ready to move fast, you’ll feel at home at MX. Job Summary As a Senior Analytics Engineer within the Operational Analytics department, you’ll play a key role in transforming complex, raw data into reliable and performant data products that power insights across MX. You’ll combine deep technical expertise in SQL, data modeling, and cloud-based data warehouses (such as Google BigQuery) with a strong sense of data stewardship, ensuring accuracy, accessibility, and trust in the analytics that drive business and product decisions. This role is ideal for a data professional who thrives at the intersection of engineering and analytics—someone who can architect and maintain scalable data models, enforce high standards for data quality, and collaborate closely with cross-functional partners to enable data-driven decisions. As a trusted internal expert, you’ll lead by example through mentorship, documentation, and process innovation, helping elevate data practices across the organization. Job Duties Data Stewardship: Design, build, and maintain data pipelines and models that transform raw data into reliable, production-ready datasets. Manage and document data definitions, lineage, and transformations using GitLab or similar tools. Data Quality and Governance: Establish and monitor data quality tests to ensure completeness, accuracy, and consistency. Partner with business stakeholders, IT, and data engineering teams to define and enforce governance standards. Data Accessibility and Democratization: Develop intuitive, business-friendly data models and assets optimized for analytics. Ensure the right data is available to the right people at the right time, empowering self-service analytics and operational reporting. Feature Store and Data Product Development: Curate and maintain high-value datasets and features in the Feature Store to support analytical and machine learning use cases. Track usage metrics and continually optimize for performance and impact. Collaboration and Mentorship: Partner cross-functionally with analysts, engineers, and product teams to define data