Senior Analytics Engineer

Mercury; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Mercury is hiring a Senior Analytics Engineer to accelerate our data foundation. You’ll build scalable data pipelines and dimensional data marts, collaborating with Data Scientists, Product, Engineering, and Operations.

You’ll advance agentic tooling and self-service analytics while shaping governance, quality, and security across analytics products. You’ll work with a modern stack including Fivetran, Airflow, Snowflake, dbt, Omni, and Hex, applying SQL and Python to deliver scalable,

Qualifications

• 4+ years of Analytics or Data Engineering experience.
• Experience with a modern data stack including Fivetran, Airflow, Snowflake, dbt, Omni, Hex or equivalents.
• Proficient in SQL and Python.
• Experience using AI agents to accelerate work.
• Experience with dimensional data modeling for scale.
• Treat data products as a platform with reusable, scalable deliverables.
• Deliver readable code with strong tests and documentation.

Responsibilities

• Design and build scalable data pipelines and dimensional data marts with cross-functional teams.
• Support development and adoption of agentic tooling (Hermes and Ralph).
• Support self-service analytics workflows and Analytics Engineering practices.
• Help define data and analytics products needed to meet regulatory or charter requirements.
• Contribute to data quality, governance, and security strategies.
• Contribute to Analytics Engineering standards and best practices.

Skills

SQL

Python

AI agents

Dimensional modeling

Data engineering

Data products

Code quality

Tools

Fivetran

Airflow

Snowflake

dbt

Omni

Hex

Job description

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.

We are hiring a Senior Analytics Engineer to help us accelerate. You’ll join a team of high‑performing Data and Analytics Engineers building the shared foundations that power decisioning, automation, and measurement across the company, collaborating closely with Data Scientists and partners in Product, Engineering, and Operations. Your curiosity and bias toward action will drive meaningful impact as you build durable data products, unlock faster experimentation, and help teams ship propensity models, agentic workflows, and amazing data‑driven experiences for our customers. Come grow with us.

Responsibilities

• 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

You may be a good fit if you:

• 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

Strong candidates may additionally have:

• Banking* or financial services industry experience
• Experience with agentic development and/or analytics workflows