Senior Data Engineer

Job Description

Job Description

PLUM is a fintech company empowering financial institutions to grow their business through a cutting-edge suite of AI-driven software, purpose-built for lenders and their partners across the financial ecosystem. We are a boutique firm, where each person’s contributions and ideas are critical to the growth of the company.

This is a fully remote position, open to candidates anywhere in the U.S. with a reliable internet connection. While we gather in person a few times a year, this role is designed to remain remote long-term. You will have autonomy and flexibility in a flat corporate structure that gives you the opportunity for your direct input to be realized and put into action. You'll collaborate with a high-performing team — including sales, marketers, and financial services experts — who stay connected through Slack, video calls, and regular team and company-wide meetings. We’re a team that knows how to work hard, have fun, and make a meaningful impact—both together and individually.

Job Summary

We are seeking a Senior Data Engineer to lead the design and implementation of scalable data pipelines that ingest and process data from a variety of external client systems. This role is critical in building the data infrastructure that powers Plum’s next-generation AI-driven products.

You will work with a modern data stack including Python, Databricks, AWS, Delta Lake, and more. As a senior member of the team, you’ll take ownership of architectural decisions, system design, and production readiness—working with team members to ensure data is reliable, accessible, and impactful.

Key Responsibilities

• Design and architect end-to-end data processing pipelines : ingestion, transformation, and delivery to the Delta Lakehouse.
• Integrate with external systems (e.g., CRMs, file systems, APIs) to automate ingestion of diverse data sources.
• Develop robust data workflows using Python and Databricks Workflows.
• Implement modular, maintainable ETL processes following SDLC best practices and Git-based version control.
• Contribute to the evolution of our Lakehouse architecture to support downstream analytics and machine learning use cases.
• Monitor, troubleshoot, and optimize data workflows in production.
• Collaborate with cross-functional teams to translate data needs into scalable solutions.

Requirements

Preferred Qualifications

Benefits