Principal Data Engineer, Analytics

Overview

Principal Data Engineer, Analytics responsible for building and maintaining data products that provide actionable insights for internal teams and external partners.

What you'll do

• Own the end-to-end lifecycle of analytics data products from architecture through production deployment and maintenance.
• Build complex dbt models and data transformation logic for high-volume financial datasets.
• Write production-grade Python for advanced data processing, anomaly detection, and orchestration logic.
• Improve query performance by refactoring legacy code and optimizing incremental loading strategies.
• Implement deployment/reporting pipelines with Git and dbt Cloud and manage robust version control.
• Create automated data quality tests/validation suites using dbt tests and Python.
• Lead major technical initiatives and partner with Product, Finance, Operations, Risk, and Trading.

What you'll need

• 8+ years of professional experience in analytics engineering or data engineering.
• Expert proficiency in SQL for optimizing complex queries and data models at scale.
• Advanced Python proficiency for data manipulation and interaction with APIs/AWS services.
• Experience with Databricks for analytics workloads and modeling with Databricks SQL and dbt.
• Experience with dbt materializations, macros, and package management.
• Experience architecting data models for FinTech/Capital Markets (trade lifecycles, clearing/settlement, risk models, reporting).
• Bachelor’s degree in Computer Science, Software Engineering, or related technical field.