Senior Data Analytics Engineer

Step is a financial technology company focused on improving the financial future of the next generation through banking and borrowing products. The Senior Data Analytics Engineer will own analytics data models, pipelines, metric definitions, and semantic layers while partnering with Product, Marketing, Engineering, Risk, Growth, and Finance to build a trustworthy data foundation.

Responsibilities:

• Own and maintain Step's core analytics data models, the transformation layer connecting raw user, product, banking, and risk data to every dashboard and BI tool at the company, serving as the single source of truth teams make decisions from
• Design, build, and maintain the pipelines and transformations powering analytics and data science use cases, with data quality, freshness, and lineage checks built in rather than bolted on afterward
• Provide expert guidance, recommendations, and influence to Product, Risk, Marketing, Growth, and Finance partners on metric definitions and instrumentation, leveraging an in-depth understanding of how Step's products generate data
• Build and maintain the semantic layer underneath our BI tools, so the dashboards analysts and stakeholders rely on are built on a consistent, well-modeled foundation
• Design and build using AI-augmented workflows, including model scaffolding, test and documentation generation, and first-pass exploratory analysis, to make our work faster, deeper, and more accurate
• Create reusable query, visualization, and code components that let teams answer their own questions without waiting on the Data team
• Collaborate with data scientists, analysts, and platform engineers as the Data team grows, helping to align modeling standards with broader engineering considerations

Requirements:

• 6+ years of experience across analytics engineering, data engineering, and analytics roles with real modeling ownership, including time at a consumer product or fintech company
• Deep expertise in SQL and analytics data modeling, with extensive experience building data models and pipelines from scratch rather than inheriting them
• Hands-on experience with BI and analytics tools such as Looker, Mixpanel, or Tableau — including building the semantic layer underneath them, not just the dashboards on top
• An obsession with quality — you test your models, you check your own work, and you deliver numbers that executives can trust without re-checking
• Hands-on experience making AI part of how you work — you already use AI tools daily to build faster and go deeper, you've automated real pieces of your own workflow, and you have a point of view on where AI can transform analytics engineering
• A demonstrated ability to take full ownership of a workstream and drive it independently from question to decision, without significant supervision
• A bias toward simple, effective solutions — you resist complexity that hasn't earned its keep, and you'd rather ship a clear model than a clever one
• Excellent written and verbal communication, including the ability to distill complex data topics for non-technical audiences
• Proficiency in at least one programming language (Python preferred) and fluency with modern transformation and orchestration tooling
• Education: Bachelor's degree or an equivalent combination of education, training, and experience
• Experience with consumer fintech, banking, or lending data — card transactions, ledgers, credit performance, or fraud and risk signals
• Familiarity with dbt, BigQuery, Looker, and data observability tooling
• Experience setting up CI and testing frameworks for data pipelines