Senior Analytics Engineer — Build Scalable Data Platforms

Mercury; New York (NY); On-site; USD 167,000 - 208,000; Full time

Job summary

Mercury is hiring a Senior Analytics Engineer to accelerate data platforms powering decisioning, automation and measurable data experiences for customers. You will design scalable pipelines, build data marts, and support self-service analytics across Data Science, Product and Operations.

You will work with a modern stack (Fivetran, Airflow, Snowflake, dbt, Omni, Hex) and apply dimensional modeling, tests and documentation to deliver reusable data products at scale.

Qualifications

• 4+ years of Analytics or Data Engineering experience.
• Experience with a modern data stack including Fivetran / Airflow / Snowflake / dbt / Omni / Hex or equivalents.
• Proficient with SQL and Python.
• Experience using AI agents to accelerate work.
• Experience with dimensional data modeling and building data for scale.

Responsibilities

• Design and build scalable data pipelines and data marts.
• Support the development and adoption of agentic tooling (Hermes, Ralph).
• Support self-service analytics workflows and analytics engineering skills.
• Help implement data and analytics products to power the bank charter.
• Contribute to data quality, governance, and security strategies.
• Define Analytics Engineering standards and best practices.

Skills

Analytics experience

SQL

Python

AI agents

Dimensional modeling

Readable code

Testing

Documentation

Stakeholder collaboration

Prioritization

Tools

Fivetran

Airflow

Snowflake

dbt

Omni

Hex

Job description

Mercury is hiring a Senior Analytics Engineer to accelerate data platforms powering decisioning, automation and measurable data experiences for customers. You will design scalable pipelines, build data marts, and support self-service analytics across Data Science, Product and Operations.

You will work with a modern stack (Fivetran, Airflow, Snowflake, dbt, Omni, Hex) and apply dimensional modeling, tests and documentation to deliver reusable data products at scale.