Staff Software Engineer - Data Infrastructure
Plaid is a financial technology company providing APIs and network connectivity for businesses to build financial products. Its platform supports bank-account linking, financial data access, identity verification, fraud and risk tools, credit underwriting, and bank payments.
Maintainer signals as of 8/12/2026
Funding history
Projects
About Plaid
Plaid operates a financial data network and API platform that lets businesses connect to financial institutions and build financial experiences. Its products support account and identity verification, real-time balance and transaction data, investment and liability data, income and underwriting workflows, fraud and AML risk checks, and multi-rail bank payments. It serves developers, businesses, financial institutions, platforms, lenders, banks, and consumer-facing financial-product providers.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will shape data infrastructure roadmaps and deliver systems for data warehouses, lakehouses, Spark, workflow orchestration, and streaming. You will improve machine-learning development paths and data freshness, reduce operational burden, collaborate across functions, and mentor engineers through technical reviews and guidance.
Requirements
- 6+ years of software engineering experience
- Hands-on software engineering experience delivering projects in data infrastructure or platform domains
- Deep understanding of data warehouses, data lakehouses, Apache Spark, streaming infrastructure, or workflow orchestration
- Strong cross-functional collaboration and communication skills
- Project management skills
- Proficiency in coding, testing, and system design
- Experience mentoring and guiding junior engineers
- Experience with Databricks, Airflow, AWS EMR, or Python
Responsibilities
- Contribute to the long-term roadmap for data-driven and machine-learning iteration
- Lead data infrastructure projects involving ML development paths, streaming, ETL, warehouses, and lakehouses
- Define technical roadmaps for backend systems and abstractions with stakeholders
- Debug and troubleshoot the Data Platform
- Reduce operational burden
- Mentor engineers and review technical documents and code changes
Benefits
- Equity