Senior Software Engineer - Data Infrastructure

Plaid

🔍 Observed

$180K - $270K USD

San Francisco First seen 3 weeks ago Last seen 5 hours, 39 minutes ago Lever

Apply on Lever Search Google for This Role

ATS links often expire — Google search finds the latest posting

Job Description

AI Summary

• Designs and scales data infrastructure systems including data warehouses, lakehouses, and streaming platforms to enable data-driven decisions across engineering, product, and business teams at Plaid. • Requires 5+ years of software engineering experience with deep expertise in ML infrastructure, data infrastructure systems, Apache Spark, workflow orchestration, or similar technologies. • Leads major infrastructure projects such as ML development improvements, streaming solutions, ETL pipeline development, and data platform evolution while mentoring junior engineers. • Must demonstrate strong system design, cross-functional collaboration, and proven ability to deliver successful projects at similar-scale companies. • Nice-to-have skills include experience with Databricks, Airflow, and AWS EMR; role offers opportunity to shape long-term technical roadmap at a fintech company.

Making data driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide tooling and guidance to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively. We build the data and machine learning infrastructure to enable Plaid engineers to prototype and iterate on products and features built on top of consumer-permissioned financial data.

Engineers on Data Infrastructure are domain experts in Data Warehouse, Data Lakehouse, Spark, Workflow Orchestration, and Streaming technologies. We scale our existing data pipelines in a performant and cost efficient way while creating the necessary abstractions to make developing on top of this platform extremely simple for other engineers at Plaid.

Responsibilities: Contribute towards the long-term technical roadmap for data-driven and machine learning iteration at Plaid Leading key data infrastructure projects such as improving ML development golden paths, implementing offline streaming solutions for data freshness, building net new ETL pipeline infrastructure, and evolving data warehouse or data lakehouse capabilities. Working with stakeholders in other teams and functions to define technical roadmaps for key backend systems and abstractions across Plaid. Debugging, troubleshooting, and reducing operational burden for our Data Platform. Growing the team via mentorship and leadership, reviewing technical documents and code changes. Qualifications: 5+ years of software engineering experience Extensive hands-on software engineering experience, with a strong track record of delivering successful projects within the Data Infrastructure or Platform domain at similar or larger companies. Deep understanding of one of: ML Infrastructure systems, including Feature Stores, Training Infrastructure, Serving Infrastructure, and Model Monitoring OR Data Infrastructure systems, including Data Warehouses, Data Lakehouses, Apache Spark, Streaming Infrastructure, Workflow Orchestration. Strong cross-functional collaboration, communication, and project management skills, with proven ability to coordinate effectively. Proficiency in coding, testing, and system design, ensuring reliable and scalable solutions. Demonstrated leadership abilities, including experience mentoring and guiding junior engineers. [Nice to have] Experience with Databricks, Airflow, AWS EMR

Quick Actions

Apply Externally Search Google Back to Jobs

Job Information

Company:
Plaid
Location:
San Francisco
Job Type:
Full-Time
Experience Level:
Senior
Source:
Lever
Status:
Active

Activity Score

65 /100

Likely Active (65)

Higher scores indicate more likely active hiring based on listing freshness, company activity, and other signals. Learn more →