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Salary: USD 166,600 - 208,300 / annual
Mercury is a fintech platform providing banking, treasury, and financial workflow solutions for startups and technology companies. The company is building an AI-native data platform to enable reliable analytics, accelerate product development, and power next-generation AI-driven products and internal tools.
You will join a high-performing Data and Analytics Engineering team responsible for building shared data foundations that power decisioning, automation, and measurement across the company. You'll collaborate closely with Data Scientists and partners in Product, Engineering, and Operations to design and build scalable data pipelines, support agentic tooling (including Mercury's proprietary AI Data Analyst "Hermes" and dbt Agent "Ralph"), and unlock faster experimentation.
Key responsibilities include:
- Design and build scalable data pipelines and business-conformed dimensional data marts in collaboration with cross-functional teams
- Support the development and adoption of agentic tooling built and managed by Analytics Engineers
- Enable self-service analytics workflows and dimensional data principles through implementation, education, and peer support
- Implement data and analytics products needed for the company's bank charter
- Contribute to data quality, governance, and security strategies
- Define Analytics Engineering standards and best practices
Your curiosity and bias toward action will drive meaningful impact as you build durable data products and help teams ship propensity models, agentic workflows, and data-driven customer experiences.
Requirements:
- 4+ years of Analytics or Data Engineering experience
- Expertise with modern data stack tools (Fivetran, Airflow, Snowflake, dbt, Omni, Hex, or equivalents)
- Proficiency in SQL and working experience with Python
- Proficiency using AI agents to accelerate work
- Experience with dimensional data modeling principles and building data for scale
- Ability to treat data products as a platform, prioritizing reusable, scalable deliverables
- Strong code quality, testing, and documentation practices
- Responsible experimentation and knowledge-sharing mindset
- Relentless empathy for stakeholders across Data, Product, Engineering, and Operations
- Ability to discern what's needed from what's wanted to maximize impact
Strong candidates may additionally have:
- Banking or financial services industry experience
- Experience with agentic development and analytics workflows
- Exposure to data governance, compliance, and security best practices
- Full-stack mindset and willingness to solve problems end-to-end across Data Engineering and Data Analysis
About Mercury
Fintech — banking, treasury, and financial workflow platform for startups and technology companies.
