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Data Engineernew

Greenlight · Fintech & Payments

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Greenlight is a family technology company on a mission to help families raise financially smart kids and navigate life together. Its suite of products — including the Greenlight app, debit card, Safe Family GPS trackers, and Family Hub — brings together financial tools, safety features, and everyday family management in one connected experience. Designed for busy families who want convenience without compromise, Greenlight continues to innovate alongside the evolving needs of modern life. Today, more than 6.5 million family members trust Greenlight to stay in sync.

At Greenlight, we're here to make the day-to-day easier and futures brighter, so families can spend less time managing life, and more time living it. That's why we get out of bed every morning.


We are looking for a Data Engineer to join our Data Platform team to partner with our product and business stakeholders across risk, operations, and other domains. We are looking for someone who thrives in taking on complex data infrastructure problems with the initiative, problem-solving, and curiosity needed to build reliable and scalable solutions. This role will focus on building robust data pipelines and engineering foundations by ingesting data from disparate sources, ensuring data quality and consistency, and enabling better business decisions through reliable data infrastructure across core product areas.
 
This is the perfect opportunity for an individual passionate about data engineering, who has the drive to solve complex technical problems and has the ability to grow alongside our team and our company. The ideal candidate can quickly identify data quality issues and bottlenecks, develop creative engineering solutions, and demonstrate the ability to effectively communicate technical decisions to stakeholders. We are seeking someone who is comfortable collaborating directly with data analysts, data scientists, and business stakeholders to understand requirements and strategically architect and implement data solutions — especially when the path forward isn't fully defined. This will include data pipeline development, data modeling, orchestration design, infrastructure automation, and data quality monitoring.

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