Senior Analytics Engineer

Mercury

. Design and build scalable data pipelines and business-conformed dimensional data marts with Data Science, Engineering, Product, and Operations .

Posted 10/5/2026full-timeRemote • United States, CanadaSenior💰 $166,600 - $208,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building scalable data pipelines and implementing analytics engineering best practices, with a strong focus on data quality, governance, and security. Proficient in utilizing modern data stack tools to support self-service analytics and enhance data-driven decision-making.

Highest-signal resume keywords

Analytics EngineeringData EngineeringSQL ProficiencyPython ExperienceDimensional Data Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DesignDimensional Data Mart DevelopmentSQLPythonData Quality StrategiesData GovernanceData SecurityAgentic DevelopmentAnalytics WorkflowsReusable Deliverables

Soft Skills

Problem-SolvingCollaborationEducation and Peer Support

Tools & Technologies

FivetranAirflowSnowflakeDbtOmniHexAI Data AnalystHermesDbt AgentRalph

Industry Keywords

BankingFinancial ServicesData Governance ComplianceData-Driven Customer Experiences

Tech Stack

Tools & technologies

AirflowPythonSQL

About the role

Key responsibilities & impact

Design and build scalable data pipelines and business-conformed dimensional data marts with Data Science, Engineering, Product, and Operations
Support development and adoption of agentic tooling, including Mercury’s AI Data Analyst (Hermes) and dbt Agent (Ralph)
Support self-service analytics workflows, Analytics Engineering skills, and dimensional data principles through implementation, education, and peer support
Implement data and analytics products needed to effect Mercury’s bank charter
Contribute to data quality, governance, and security strategies
Contribute to Analytics Engineering standards and best practices
Build shared data foundations powering decisioning, automation, and measurement across Mercury
Enable durable data products, faster experimentation, propensity models, agentic workflows, and data-driven customer experiences

Requirements

What you’ll need

4+ years of Analytics or Data Engineering experience
Expertise working in a full modern data stack including Fivetran, Airflow, Snowflake, dbt, Omni, Hex, or equivalents
Proficiency with SQL
Working experience with Python
Proficiency using AI agents to accelerate work
Experience with dimensional data modeling principles and building data for scale
Ability to prioritize reusable, scalable deliverables
Ability to deliver readable code, strong tests, and quality documentation
Banking or financial services industry experience may be an additional qualification
Experience with agentic development and/or analytics workflows may be an additional qualification
Exposure to data governance, compliance, and security best practices may be an additional qualification
Full-stack mindset and willingness to solve problems end-to-end may be an additional qualification

Benefits

Comp & perks

Base salary
Equity (stock options/RSUs)
Benefits
Reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs