Staff Analytics Engineer

Summary

Join Mercury as their first Staff Analytics Engineer and play a pivotal role in shaping their data processing and understanding. You will contribute to evolving data quality, governance, and security strategies, building scalable data pipelines, and collaborating with cross-functional teams. This role requires 7+ years of Analytics or Data Engineering experience, expertise with a modern data stack, and proficiency in SQL, dbt, and Python. You will mentor team members and foster a continuous learning culture. Mercury offers a competitive total rewards package including base salary, equity, and benefits.

Requirements

• 7+ years of Analytics or Data Engineering experience
• Expertise with A full modern data stack (Fivetran / Snowflake / dbt / Metabase / Hex or equivalents)
• SQL, dbt, Python
• Experience with OLAP data modelling and architecture in support of self-service
• Experience with Streaming / real-time data pipelines
• Experience with Least privilege access patterns across data warehouse and visualization tooling

Responsibilities

• Contribute to the evolution of Mercury’s data quality, governance, and security strategies
• Contribute to the evolution of Data Team best practices and workflows to ensure that we are building a sustainable and scalable data function
• Drive adoption of new tools and methodologies to enhance data accessibility, discovery and documentation
• Design and build scalable data pipelines and business-conformed data marts that enable better decision-making and data self-service
• Collaborate with cross-functional teams, including Engineering, Data Science, Product, Risk, InfoSec and Marketing to understand the needs of the business and how to serve them
• Mentor team members and foster a culture of continuous learning
• Support stakeholders to encourage data literacy in every department at Mercury
• Partner with Data Team leadership to align on departmental priorities

Preferred Qualifications

• Experience with OLTP data
• Experience with zero trust access pattern
• Exposure to Serving data for ML and Generative AI use cases
• Exposure to The financial services industry (banking, insurance, accounting, or payments)
• Exposure to Data compliance standards (CCPA, GDPR, et al. )
• Experience implementing operational controls to comply with data compliance standards

Benefits

• Base salary
• Equity (stock options)
• Benefits