About the Company:

Sunbit builds financial technology for real life. Our technology eases the stress of paying for life’s expenses by giving people more options on how and when they pay. Founded in 2016, Sunbit offers a next-generation, no-fee credit card that can be managed through a powerful mobile app, as well as a point-of-sale payment option available at more than 25,000 service locations, including auto dealership service centers, optical practices, dentist offices, veterinary clinics, and specialty healthcare services. Sunbit was included on the 2022 Inc. 5000 list. The financial technology company has also been named a Most Loved Workplace®, Best Point of Sale Company, and a Top Fintech Startup by CB Insights.

We use cutting-edge innovations in financial technology to bring leading data and features that allow individuals to be qualified instantly, making purchases at the point-of-sale fast, fair and easy for consumers from all walks of life.

As part of our Data Engineering team, you will not only build scalable data platforms but also directly enable portfolio growth by supporting new funding capabilities, loan sales and securitization, and improving cost efficiency through automated and trusted data flows that evolve our accounting processes.

Responsibilities

• Design and build data solutions that support Sunbit’s core business goals, from enabling capital market transactions (loan sales and securitization) to providing reliable insights for reducing the cost of capital.
• Develop advanced data pipelines and analytics to support finance, accounting, and product growth initiatives.
• Create ELT processes and SQL queries to bring data to the data warehouse and other data sources.
• Develop data-driven finance products that accelerate funding capabilities and automate accounting reconciliations.
• Own and evolve data lake pipelines, maintenance, schema management, and improvements.
• Create new features from scratch, enhance existing features, and optimize existing functionality.
• Collaborate with stakeholders across Finance, Product, Backend Engineering, and Data Science to align technical work with business outcomes.
• Implement new tools and modern development approaches that improve both scalability and business agility.
• Ensure adherence to coding best practices and development of reusable code.
• Constantly monitor the data platform and make recommendations to enhance architecture, performance, and cost efficiency.

Requirements:

• 4+ years of experience as a Data Engineer.
• 4+ years of Python and SQL experience.
• 4+ years of direct experience with SQL (Redshift/Snowflake), data modeling, data warehousing, and building ELT/ETL pipelines (DBT & Airflow preferred).
• 3+ years of experience in scalable data architecture, fault-tolerant ETL, and data quality monitoring in the cloud.
• Hands-on experience with cloud environments (AWS preferred) and big data technologies (EMR, EC2, S3, Snowflake, Spark Streaming, Kafka, DBT).
• Strong troubleshooting and debugging skills in large-scale systems.
• Deep understanding of distributed data processing and tools such as Kafka, Spark, and Airflow.
• Experience with design patterns, coding best practices, and data modeling.
• Proficiency with Git and modern source control.
• Basic Linux/Unix system administration skills.

Nice to Have

• Familiarity with fintech business processes (funding, securitization, loan servicing, accounting).- Huge advantage
• BS/MS in Computer Science or related field.
• Experience with NoSQL or large-scale DBs.
• DevOps experience in AWS.
• Microservices experience.
• 2+ years of experience in Spark and the broader Data Engineering ecosystem.

What Else

• Energetic and data-enthusiastic mindset.
• Ability to translate complex technical work into business impact.
• Analytical and detail-oriented.
• Strong communication skills with both technical and business teams.
• Self-motivated, fast learner, and team player.