Lead Analytics Engineer

Koalafi•Arlington, VA

1d•Onsite

About The Position

At Koalafi, we believe in a world where no one has to put an important purchase on hold. That’s why we’re making it easier for more people to pay for big purchases over time. Retailers across the country rely on us to offer flexible lease-to-own financing to their non-prime consumers, while increasing sales and strengthening customer loyalty. Their 2M+ customers love us because we provide a flexible way for them to make payments and give them an opportunity to improve their credit. Our 200+ Koalafi teammates enjoy inspiring and challenging work that accelerates their careers. Interested in learning more about how we’re transforming the financing experience and joining our team? Our data platform team is looking for a Lead Analytics Engineer to drive the design and development of enterprise data products. This is a technical leadership role where you'll own high-value project outcomes end-to-end. You'll scope initiatives, organize delivery for yourself and team members, and build production datasets that power business intelligence and analytics across the organization. As the senior technical contributor on the analytics engineering team, you'll set the standard for hands-on delivery and quality. You'll partner closely with analysts, business stakeholders, and data engineers to translate complex business needs into scalable, well-modeled datasets using modern tools like dbt and Snowflake. The Lead Analytics Engineer will apply proven technical expertise, project leadership, and problem-solving skills to drive team success and deliver quality data solutions.

Requirements

Four-year college or university program certificate in Computer Science/Engineering or Information Systems; or 8+ years related experience and/or training; or equivalent combination of education and experience
8+ years hands-on experience delivering data solutions in production
5+ years contributing to a data, analytics, or BI engineering team
Proven track record leading data projects end-to-end, including requirements gathering, scoping, design, and stakeholder management
Deep expertise in data warehousing techniques: dimensional modeling, slowly changing dimensions, fact/dimension design
Expert-level SQL skills with experience writing complex, performant queries
Production experience with dbt (dbt Core or dbt Cloud)
Experience with Snowflake or comparable cloud data warehouse
Experience preparing and optimizing data for consumption by BI/reporting tools (Tableau, Omni, Looker, or similar)
Experience mentoring or leading other engineers
Exceptional analytical, conceptual, and problem-solving abilities
Highly self-motivated and directed, with keen attention to detail
Ability to drive towards outcomes that meet stakeholder needs
Knowledge of scrum/agile software development process
Experience with source control tools such as Git, and familiarity with CI/CD concepts
Location Requirement: This position requires regular in-person attendance at one of our two office locations (Richmond, VA or Arlington, VA). Candidates must already be located within a commutable distance to either location, as relocation assistance is not available at this time.

Responsibilities

Lead major enterprise data initiatives from requirements gathering through design, implementation, testing, and stakeholder adoption
Own data modeling and architecture decisions for analytics-facing datasets, applying dimensional modeling, slowly changing dimensions, and modern data warehousing techniques
Design, develop, test, and deploy production data products using dbt and Snowflake that meet critical business intelligence and reporting needs
Mentor and coach junior analytics engineers, providing technical guidance, code reviews, and career development support
Establish and evolve data modeling standards, testing practices, documentation conventions, and engineering best practices across the team
Partner with BI teams to optimize data structures for reporting tool consumption (Tableau, Omni, or similar)
Collaborate with stakeholders and engineering leadership to understand requirements and create consensus on approach and outcomes
Prepare and socialize documentation of data models, pipeline specifications, and architectural decisions