Data Engineer

Leap Tools

6h•Remote

About The Position

Leap Tools is seeking a Data Engineer with a strong foundation in building and maintaining data pipelines and data models to power analytics, reporting, and product insights. The ideal candidate enjoys working close to production systems and cares deeply about data quality, reliability, and performance. This role offers an opportunity to grow impact and technical depth, take ownership of meaningful parts of the data platform, learn best practices in data warehousing, and collaborate with senior engineers, ML engineers, and product teams. Leap Tools values execution, collaboration, and learning, and is comfortable operating in a fast-paced environment where priorities evolve and ownership matters. The company emphasizes hiring individuals, not just based on job descriptions, and encourages applications even if the role and salary range don't perfectly align with experience, as unique roles and compensation can be created for the right talent. This is a net new position on the team.

Requirements

2+ years of professional experience in data engineering, analytics engineering, or backend engineering with a strong data focus.
Strong SQL skills and working proficiency in Python.
Experience building or maintaining data pipelines and working with a data warehouse or analytics stack.
Solid computer science fundamentals, based on a degree in Computer Science or equivalent practical experience.
Understanding of system architecture with an emphasis on reliability, availability, and performance.
Strong focus on execution and delivering production-ready solutions.
High attention to detail and commitment to data quality and long-term maintainability.
Clear communication skills and comfort collaborating across teams.
Comfortable working in a fast-paced, evolving environment where learning and adaptability are key.

Nice To Haves

Familiarity with our stack is strongly preferred but not required.

Responsibilities

Design, build, and maintain reliable ELT pipelines that ingest and transform data into our centralized data warehouse.
Model data for analytics and reporting use cases, ensuring performance, correctness, and usability for downstream consumers.
Partner with Product, ML Engineers, Data Scientists, and DevOps to support data needs across the organization.
Take ownership of specific data pipelines, datasets, or warehouse components, including monitoring, debugging, and iterative improvement.
Help improve data quality, testing, and documentation standards within the data platform.
Participate in system design discussions and code reviews, learning from senior engineers and contributing your perspective.
Unblock and support internal stakeholders by making data accessible, understandable, and trustworthy.