Team Leader, Analytics Engineering

ABOUT ROCKET MONEY

Rocket Money’s mission is to meaningfully improve the financial prosperity of millions of people. Rocket Money offers members a unique understanding of their finances and a suite of valuable services that save them time and money – ultimately giving them a leg up on their financial journey.

Please note: We strongly encourage team members to be in the office 1-2 days per week, though there is no formal in-office requirement.

ABOUT THE TEAM

Analytics Engineers at Rocket Money further our mission by helping our company understand our users and products through the lens of data. We build data models that uncover how our customers interact with our products, generate cashflows, and traverse our user experiences. We work with our product and engineering teams to understand the data our applications generate, model it in useful, understandable ways, and make that data available for efficient insight generation.

Our team values:

• Collaboration - We're team players who excel at cross-functional partnership with product and engineering teams
• Impact - We transform raw data into strategic insights that drive business decisions
• Growth - We continuously refine our craft while supporting each other's professional development

We're growing our team and are looking for an Analytics Engineering Team Leader who is committed to building an equitable, healthy work environment where team members thrive. In this role, you'll elevate our Analytics Engineering practice, mentor talented engineers, be a hands on contributor to our dbt and LookML code bases, and champion data-driven decision making across the organization. You'll help shape how Rocket Money uses data to understand our customers and products while building a world-class team.

Our Tech Stack: BigQuery, dbt, Looker, Amplitude, Fivetran, Datadog, git, Deepnote

 

IN THIS ROLE, YOU'LL:

• Own ambitious yet achievable quarterly roadmaps that drive our business forward by building tooling that allows the team to deliver faster, more consistent work and directing cross-team analytics efforts that inform strategy.
• Lead and manage a team of 4-6 analytics engineering professionals, creating processes for autonomous operation with cross-functional teams while maintaining high quality standards and accountability and supporting individual growth.
  • Evolve team development standards, including technical skill frameworks, career ladders, and learning pathways. Create a culture of continuous improvement through code reviews, knowledge sharing sessions, and mentorship programs that elevate the entire team's capabilities.
  • Champion data quality and attention to detail as core team values, establishing review processes and quality gates that ensure the accuracy and reliability of analytics outputs while still shipping iteratively and building stakeholder trust through visible progress.
• Foster strong working relationships with other leaders at the company, advocating for your team's involvement at the right time and driving high quality analytics delivery across teams.
• Address problems with performance directly and make decisions based on what's best for our users, team members, and business. Create highly functional teams and processes that make best use of data analytics resources.
• Be accountable for making, measuring, and delivering on team commitments, as well as pivoting when necessary to keep the team focused on the most impactful work.
• You're energized by problems that don't come with clearly defined requirements, and you know how to work with stakeholders to scope what's actually measurable.

 

ABOUT YOU

Technical Leadership

• You have hands-on experience with the modern analytics stack (e.g., dbt, Snowflake/BigQuery, Looker/Tableau/Databricks/Omni) and can guide technical decisions while establishing quality standards and review processes that ensure data accuracy.
• You have deep expertise in analytics engineering and a strong understanding of data systems and processes, especially delivery of maintainable/scalable models that meet stakeholder needs and support self serv