Senior Data Engineer

Requirements

• Bachelors' or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field
• A minimum of 2 years of work experience in data engineering
• Hands-on experience in building robust data models and data pipelines
• A self-starter with a bias for action and excellent communication skills, with ability to explain complex technical concepts in easy-to-understand ways
• Expert knowledge of SQL, and popular data engineering tools such as snowflake, dbt, Dagster etc
• Experience working with DS team
• A humble collaborative can-do attitude and natural curiosity
• Base Range
• $226,000 - $254,000 USD
• Equal Employment Opportunity Statement
• Please reference the following for more information .

Additional Information

Kikoff: The Fintech Powering Financial Security at Scale Kikoff is a profitable, pre-IPO fintech company on a mission to empower everyone to achieve financial security. With record revenue growth in 2025 and a unicorn valuation, we've built a suite of products that help millions of people build credit, access liquidity, and save money. We're scaling fast. Join us if you want to build something meaningful and help millions of people move forward financially. Why Kikoff: This is a consumer fintech startup, and you will be working with serial entrepreneurs who have built strong consumer brands and innovative products. We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships. Yes, you can build an exciting business AND have real-life real-customer impact. We are looking for a Data Engineer or Analytics Engineer to join our Data team. You will collaborate with the data scientist and engineers to design, build, and scale high-leverage data models, foundational datasets and scalable infrastructure that enables analytics, modeling, and experimentation. Your responsibilities includes: Build and optimize high-quality ergonomic foundational datasets and the relevant data pipelines Establish data quality management processes at scale Build analytical dashboards

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