Analytics Engineer

Mercury
San Francisco, CA, United States
7 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
4 years minimum
Compensation
$166,600.0 - $208,300.0
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Airflow Data Analysis Information Engineering Data Governance Data Warehousing Python (Programming Language) SQL Databases Snowflake Agentic-AI Build Management Data Analytics
+1 more
Data Pipelines

Job description

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

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
  • 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
  • Exposure to data governance, compliance, and security best practice
  • A full-stack mindset and willingness to solve problems end-to-end by flexing into Data Engineering and Data Analysis

Benefits & conditions

The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.

Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.

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