Data Engineer
10 Months ago • All levels • $180,000 PA - $270,000 PA
Data Analysis
Job Description
Plaid is a company transforming how people interact with their finances by empowering developers with tools to build financial products. We aim to improve financial health for millions of users, partnering with companies like Venmo and major banks. Our Data Engineering team focuses on building robust datasets to drive business insights and data-driven decision-making. We scale data systems while ensuring data accuracy and completeness, providing tools for teams across engineering, product, and business to explore data. Data Engineers primarily use SQL and Python, leveraging tools like DBT, Airflow, Redshift, and ElasticSearch. We foster an IC-driven culture, encouraging bottom-up ideation and team growth, with a focus on creating impact and improving existing systems. This role offers a high-impact opportunity to own and define internal datasets and visualizations, collaborate across all departments, and enhance technical skills within a strong Data Engineering and Data Platform team.
Good To Have:
- Experience with DBT, Airflow, Redshift, ElasticSearch
- Experience with Atlanta, Retool
- Data strategy development
- Data-first mindset
Must Have:
- SQL and Python proficiency
- Build data workflows
- Data-driven decision making
- Scale data systems
- Ensure data accuracy
- Collaborate with cross-functional teams
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. #LI-Hybrid
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.
You will be in a high impact role that will directly enable business leaders to make faster and more informed business judgements based on the datasets you build. You will have the opportunity to carve out the ownership and scope of internal datasets and visualizations across Plaid which is a currently unowned area that we intend to take over and build SLAs on. You will have the opportunity to learn best practices and up-level your technical skills from our strong DE team and from the broader Data Platform team. You will collaborate with and have strong and cross functional partnerships with literally all teams at Plaid from Engineering to Product to Marketing/Finance etc.
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