In 1989, Tim Berners-Lee envisioned a system to help CERN scientists share information more effectively. That vision became the World Wide Web – transforming how humanity connects and shares knowledge. Today, we're looking for someone who can architect the next evolution in how Mercury processes, understands, and acts on data.
We’re hiring a Senior Analytics Engineer to help build the data foundation that accelerates Mercury’s transition to an AI-native analytics platform. 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 things like propensity models and agentic workflows. 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 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
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
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
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
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
If this role interests you, we invite you to explore our public demo at demo.mercury.com.
The total rewards package at Mercury includes base salary, equity, 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
Canadian employees (any location): CAD 157,400 - 196,800
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.
We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on January 22, 2024.
[Please see the independent bias audit report covering our use of Covey for more information.]
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