Senior Analytics Engineer

About the Role

The role is based in San Francisco, CA, New York, NY, Portland, OR, or Remote within Canada or the United States, offering flexibility and autonomy to work from anywhere. As a Senior Analytics Engineer at Mercury, you will be part of a high-performing team that is passionate about using data to drive business outcomes and is committed to delivering high-quality results.

What You Will Do

• 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 implement the data and analytics products needed to effect the company's bank charter
• Contribute to the evolution of the company's data quality, governance, and security strategies
• Contribute to the definition of Analytics Engineering best practice
• Collaborate with stakeholders to understand business needs and develop data products that meet those needs
• Develop and maintain technical documentation of data pipelines and architectures
• Work closely with Data Scientists to design and implement predictive models and machine learning algorithms
• Stay up-to-date with industry trends and emerging technologies in data engineering and analytics

What We Are Looking For

• 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 and working experience with Python
• Experience with dimensional data modeling principles and building data for scale
• Strong understanding of data governance, quality, and security principles
• Ability to deliver readable code, strong tests, and quality documentation
• Experience with collaboration tools such as Git and agile development methodologies
• Strong communication and interpersonal skills
• Ability to work in a fast-paced environment and prioritize multiple tasks

Nice to 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

Benefits and Perks

• Competitive salary and equity package
• Comprehensive health, dental, and vision insurance
• Flexible PTO and vacation policy
• Remote work options and stipend
• Professional development and growth opportunities
• Access to cutting-edge technologies and tools
• Collaborative and dynamic work environment

• Develop a strong understanding of data engineering principles and technologies, including data pipelines, data warehousing, and data governance.
• Build a portfolio of personal projects that demonstrate your skills in data engineering and analytics.
• Stay up-to-date with industry trends and emerging technologies in data engineering and analytics.
• Practice whiteboarding and coding exercises to improve your problem-solving skills and ability to think on your feet.
• Prepare to talk about your experience with collaboration tools such as Git and agile development methodologies.
• Research the company's technology stack and be prepared to ask informed questions during the interview process.
• Be prepared to provide specific examples of how you have applied data engineering and analytics principles to drive business outcomes in previous roles.