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Join Mercury as a Senior Analytics Engineer to build an AI-native data platform that enhances reliable analytics and accelerates product development. Collaborate with teams to create scalable data solutions and drive impactful data-driven experiences.
In 1989, Tim Berners-Lee wrote a proposal for CERN. CERN lost knowledge when people left, because its information was in many systems that did not connect. His solution was simple: link documents so that all people can find them and use them. That proposal became the World Wide Web. Mercury has a similar challenge with data. Teams, models, and AI agents need data that they can find, understand, and trust. We are building an AI-native data platform that enables Mercury to have reliable analytics, accelerate product development, and enable the next generation of AI-powered products and internal tools.
- Design and build scalable data pipelines and business-conformed dimensional data marts in collaboration with Data Science, Engineering, Product, and Operations departments.
- Support the development and adoption of agentic tooling. We have our own AI Data Analyst (Hermes) and dbt Agent (Ralph) that are built and managed by our Analytics Engineers.
- Support self-service analytics workflows, Analytics Engineering skills, and dimensional data principles through implementation, education, and peer support.
- Help us implement the data and analytics products we’ll need to effect our bank charter.
- Contribute to the evolution of our data quality, governance, and security strategies.
- Contribute to our definition of Analytics Engineering standards and best practices.
- Have 4+ years of Analytics or Data Engineering experience.
- Have expertise working in a full modern data stack including Fivetran / Airflow / Snowflake / dbt / Omni / Hex or equivalents.
- Are proficient with SQL and have working experience with Python.
- Proficient using AI agents to accelerate your and your teammates’ work.
- Have experience with dimensional data modeling principles and building data for scale.
- Treat data products as a platform by prioritizing reusable, scalable deliverables.
- Deliver readable code, strong tests, and quality documentation.
- Experiment responsibly and share what you learn so everyone benefits.
- Practice relentless empathy by meeting your stakeholders in Data, Product, Engineering, and beyond where they’re at and helping them succeed.
- Discern what’s needed from what’s wanted to deliver maximum impact.
- Total rewards package includes base salary, equity (stock options/RSUs), and benefits.
- Competitive salary and equity ranges within the SaaS and fintech industry.
- New hire offers based on experience, expertise, geographic location, and internal pay equity relative to peers.
- Target new hire base salary ranges for this role are: