MercurySenior Analytics Engineer
AI in this role
Build data pipelines, dimensional data marts, and support agentic tooling and analytics workflows.
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
- 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
If this role interests you, we invite you to explore our public demo at demo.mercury.com.
*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
#LI-GC1
Total Rewards
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.
Our target new hire base salary ranges for this role are the following:
US employees (any location):$166,600—$208,300 USDCanadian employees (any location):$157,400—$196,800 CADHow we rate this
Senior Analytics Engineer at Mercury rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where analytics was part of your work. What did you do?
- Tell me about a project where dimensional modeling was part of your work. What did you do?
- Tell me about a project where pipelines was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI Agents, Data Engineering, Analytics, Dimensional Modeling, and Pipelines. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
Want an expert to read your CV for this job?
Free. Send your CV and the role you want next. We reply by email within 2 to 4 business days.
Get a free CV reviewGet new remote AI jobs (Works on AI ●●●○ or higher) by email
One email a week with the new remote AI jobs (Works on AI ●●●○ or higher), each rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.
Free. One email a week. Unsubscribe in one click.
Similar roles
Data roles that work on AI, at other companies.
What kind of AI work fits you?
Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.
Find my next step