Lead Data Engineer, Analytics & Data Products

DriveWealth; New York (NY); Hybrid; USD 220,000 - 240,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Disability insurance

Paid Parental Leave

Wellness reimbursement

Company-provided phone

Personal development allowance

Paid time off

Job summary

DriveWealth is seeking a Principal Data Engineer, Analytics in New York, hybrid. You will own end-to-end data products, curate key data sources, and drive scalable analytics pipelines using Databricks, dbt, and Python.

You will work with Product, Finance, Operations, Risk, and Trading to translate requirements into robust data solutions, while mentoring engineers and shaping the data architecture for high-volume financial data.

Qualifications

• 8+ years of professional analytics/data engineering experience.
• Proven ability to build scalable analytic data ecosystems.
• Expert SQL with large-scale data optimization.
• Strong Python (Pandas/Polars/Spark) for data manipulation.
• Experience with Databricks SQL and dbt modeling.
• Familiarity with FinTech/capital markets data models.

Responsibilities

• Own end-to-end data product lifecycle from concept to production.
• Develop complex dbt models and data transformation logic for high-volume financial datasets.
• Write production-grade Python scripts for data processing and automation.
• Optimize query performance and data loading strategies at scale.
• Lead technical projects and mentor engineers; collaborate with cross-functional teams.

Skills

SQL

Python

Databricks

dbt

APIs/AWS

Data Modeling

CI/CD

Leadership

Education

Bachelor’s degree in CS/Software Engineering

Tools

Databricks

dbt

Git

SQL tooling

Job description

DriveWealth is seeking a Principal Data Engineer, Analytics in New York, hybrid. You will own end-to-end data products, curate key data sources, and drive scalable analytics pipelines using Databricks, dbt, and Python.

You will work with Product, Finance, Operations, Risk, and Trading to translate requirements into robust data solutions, while mentoring engineers and shaping the data architecture for high-volume financial data.