Mercury is a fintech company that is looking for a Senior Analytics Engineer to help build the data foundation for their AI-native analytics platform. The role involves designing scalable data pipelines and collaborating with various departments to enhance data products and analytics workflows.
Responsibilities:
- Design and build scalable data pipelines and business-conformed dimensional data marts in collaboration with Data Science, Engineering, Product, and Operations departments
- Support adoption of agentic tooling, 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 best practice
Requirements:
- 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
- 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 stakeholders where they're at and helping them succeed
- Discern what's needed from what's wanted to deliver maximum impact
- Banking or financial services industry experience
- Experience with agentic development and/or analytics workflows
- Exposure to data governance, compliance, and security best practice
- Exposure to near real-time data pipelines like Kafka / NiFi or equivalents
- A full-stack mindset and willingness to solve problems end-to-end by flexing into Data Engineering and Data Analysis