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Plaid is a fintech infrastructure company that powers financial connectivity for millions of users and thousands of developers. The company operates a network covering 12,000 financial institutions across the US, Canada, UK, and Europe, serving customers like Venmo, SoFi, Fortune 500 companies, and major banks.
As a Senior Data Engineer, you will play a high-impact role in building and scaling Plaid's data systems to support data-driven decision-making across the organization. You will own the design and implementation of golden datasets and data pipelines that serve engineering, product, business, and analytics teams. This is an opportunity to carve out ownership of internal datasets and visualizations—currently an unowned area—and establish SLAs and best practices around data quality and reliability.
Key responsibilities include:
- Understanding Plaid's product and strategy to inform golden dataset design and data usage principles
- Owning core SQL and Python data pipelines that power the data lake and data warehouse
- Leading key data engineering projects that drive cross-functional collaboration
- Designing datasets with data quality and performance as top priorities
- Ensuring well-documented data with defined quality, uptime, and usefulness standards
- Advocating for adopting industry tools and practices at the right time
- Working with engineers, product managers, business intelligence analysts, and other teams to build Plaid's data strategy
You will leverage tools including DBT, Airflow, Redshift, Atlan, and Retool to orchestrate pipelines and define workflows. You'll have the opportunity to learn from a strong data engineering team and the broader Data Platform team while building cross-functional partnerships across all of Plaid.
REQUIREMENTS:
- 4+ years of dedicated data engineering experience solving complex data pipeline issues at scale
- Experience building data models and pipelines on large datasets (500TB to petabytes)
- Strong SQL skills and comfort with modern SQL data orchestration tools (DBT, Mode, Airflow)
- Experience with performant data warehouses and data lakes (Redshift, Snowflake, Databricks)
- Experience building and maintaining batch and real-time pipelines (Spark, Kafka)
- Understanding of schema design and ability to evolve analytics schemas on unstructured data
- Excitement about trying new technologies and producing proof-of-concepts that balance technical advancement with user adoption
- Comfort managing, deploying, and improving low-level data infrastructure
- Empathetic stakeholder engagement; ability to listen, ask the right questions, and collaboratively develop solutions
- Commitment to data privacy and integrity
