Senior Analytics Engineer

MX Technologies, Inc.•Lehi, UT

11h•Onsite

About The Position

MX is a fintech company on a mission to empower the world to be financially strong by building technology that helps banks, credit unions, and fintechs deliver smarter, more intuitive financial experiences. The company is in a phase of renewed momentum and scale, with a culture that values curiosity, accountability, and impact. As a Senior Analytics Engineer within the Operational Analytics department, you will transform complex, raw data into reliable and performant data products that power insights across MX. This role requires deep technical expertise in SQL, data modeling, and cloud-based data warehouses (like Google BigQuery), combined with a strong sense of data stewardship to ensure accuracy, accessibility, and trust in analytics. It is ideal for a data professional 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. You will also lead by example through mentorship, documentation, and process innovation, helping elevate data practices across the organization.

Requirements

Bachelor’s degree required, preferably in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
Minimum 5 years of experience in analytics engineering, data engineering, or business intelligence roles, with a proven track record of designing and delivering reliable, high-performance data products at scale.
Expert-level SQL proficiency (including advanced window functions, CTEs, subqueries, and query optimization).
Strong understanding of dimensional modeling, star/snowflake schemas, and SCD management.
Proficiency with cloud data warehouses (Google BigQuery preferred; Snowflake, Redshift, or Databricks acceptable).
Familiarity with programming languages such as Python for workflow automation and data quality checks.
Experience with modern data versioning and collaboration tools (Git, CI/CD pipelines).
Understanding of data governance, lineage, and cataloging tools (e.g., dbt, Dataform, or equivalent).
Proven ability to collaborate cross-functionally and communicate complex data concepts to non-technical audiences.
Strong analytical and problem-solving skills, with keen attention to detail and system-level thinking.
Demonstrated adaptability and perseverance in fast-paced, evolving environments.
Commitment to quality, transparency, and building trust through reliable data products.
Track record of mentoring peers and contributing to the growth of data capabilities within an organization.

Responsibilities

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.
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.
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.
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.
Partner cross-functionally with analysts, engineers, and product teams to define data requirements, identify opportunities for process improvements, and align on strategic priorities.
Provide mentorship and technical guidance to junior team members.
Stay current with emerging technologies, tools, and trends in analytics engineering, cloud computing, and data governance.
Lead or contribute to initiatives that improve scalability, efficiency, and reliability of MX’s data ecosystem.