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We build simple yet innovative consumer products and developer APIs that shape how everybody interacts with money and the financial system. The main goal of the DE team in 2024-25 is to build robust golden data sets to power our business goals of creating more insights based products. 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. Data Engineers heavily leverage SQL and Python to build data workflows. We use tools like DBT, Airflow, Redshift, ElasticSearch, Atlanta, and Retool to orchestrate data pipelines and define workflows. We work with engineers, product managers, business intelligence, data analysts, and many other teams to build Plaid's data strategy and a data-first mindset. Our engineering culture is IC-driven -- we favor bottom-up ideation and empowerment of our incredibly talented team. We are looking for engineers who are motivated by creating impact for our consumers and customers, growing together as a team, shipping the MVP, and leaving things better than we found them.
Job Responsibility:
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 datasets
Advocating for adopting industry tools and practices at the right time
Owning core SQL and Python data pipelines that power our data lake and data warehouse
Well-documented data with defined dataset quality, uptime, and usefulness
Requirements:
2+ years of dedicated data engineering experience, solving complex data pipeline issues at scale
Experience building data models and data pipelines on top of large datasets (in the order of 500TB to petabytes)
Value SQL as a flexible and extensible tool and are comfortable with modern SQL data orchestration tools like DBT, Mode, and Airflow
Nice to have:
Experience working with different performant warehouses and data lakes
Redshift, Snowflake, Databricks
Experience building and maintaining batch and real-time pipelines using technologies like Spark, Kafka
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