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# Senior Data Engineer - Data Engineering

- **Company:** Plaid Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $190,800.0 - $262,800.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Big Data, Information Engineering, Data Infrastructure, Data Warehousing, Python (Programming Language), SQL Databases, Unstructured Data, Snowflake, Apache Spark, Usage Tracking, Data Lakes, Apache Kafka, Data Pipelines, Amazon Redshift, Databricks
- **Published:** June 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=05e535445b7db63a

## About the Role

Do you have experience in Stakeholder relationship building?, * 4+ years of dedicated data engineering experience, solving complex data pipelines issues at scale.

 * You've have experience building data models and data pipelines on top of large datasets (in the order of 500TB to petabytes)
 * You value SQL as a flexible and extensible tool, and are comfortable with modern SQL data orchestration tools like DBT, Mode, and Airflow.
 * You have experience working with different performant warehouses and data lakes; Redshift, Snowflake, Databricks.
 * You have experience building and maintaining batch and realtime pipelines using technologies like Spark, Kafka.
 * You appreciate the importance of schema design, and can evolve an analytics schema on top of unstructured data.
 * You are excited to try out new technologies. You like to produce proof-of-concepts that balance technical advancement and user experience and adoption.
 * You like to get deep in the weeds to manage, deploy, and improve low level data infrastructure.
 * You are empathetic working with stakeholders. You listen to them, ask the right questions, and collaboratively come up with the best solutions for their needs while balancing infra and business needs.
 * You are a champion for data privacy and integrity, and always act in the best interest of consumers.

## Description

* Understanding different aspects of the Plaid product and strategy to inform golden dataset choices, design and data usage principles.
 * Have data quality and performance top of mind while designing datasetsLeading key data engineering projects that drive collaboration across the company.
 * Advocating for adopting industry tools and practices at the right timeOwning core SQL and python data pipelines that power our data lake and data warehouse.
 * Well-documented data with defined dataset quality, uptime, and usefulness.


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