Senior Analytics Engineer, Rocket Money

• 5+ years of experience in analytics engineering, data engineering, or related roles
• Deep data modeling expertise. You have designed dimensional models from scratch and integrated them into a cohesive analytics architecture to serve your stakeholders. You understand grain, SCD types, and fact table design tradeoffs
• You can handle complex modeling scenarios, such as conforming dimensions across business use cases, modeling many-to-many relationships, handling late-arriving and restated data, and keeping models usable as the source systems evolve
• You treat data quality as a design problem, not a cleanup task. You have built testing and monitoring into pipelines, defined SLAs or expectations with data owners, and managed the on-call/triage side of keeping data trustworthy
• You judge your work by its usability to your stakeholders over the long-term, not just by speed of delivery, underlying architecture elegance, or immediate use case
• Production dbt experience. You understand model materialization strategies, testing, macros, and how to keep a growing project maintainable
• 2+ years of experience with BI tools (Looker preferred) and an understanding of how modeling choices shape what's possible in the analytics layer
• Strong cross-functional communication skills and the ability to influence stakeholder & business decisions
• Strong problem decomposition: you can understand the underlying problem in requests that you receive and align them with our modeling roadmap and architecture

Desirable

• The Analytics Engineering team's mission is to make data consistent, trustworthy, and easy to use
• We build the dimensional models that power business decisions, and we own the integrity of the data those decisions rest on
• We work in a semi-embedded structure: Analytics Engineers sit close to the Data Analyst teams they support while remaining part of a central Analytics Engineering practice
• Day to day, that means translating business questions into technical requirements, modeling ingested source data into shapes the business can actually use, and partnering with engineering teams on the upstream data we depend on
• We're looking for a Senior Analytics Engineer to help shape that architecture as it grows
• You'll build and iterate on our star schema, surfacing models that Data Analysts and stakeholders across the business can self-serve against with confidence
• Done well, this work raises the data literacy of the entire company — and we take that seriously
• Be a trusted builder, architect, and consultant on the analytics data we surface to our stakeholders
• You'll learn the intricacies of our source data, how we transform it, how we monitor for integrity, and continue to move the needle on the partnership needed to maximize value for the business
• Design and build dimensional models that scale (we use dbt + BigQuery):
• Translate ambiguous business questions into dimensions, facts, and grain decisions that hold up as the business changes
• Make tradeoffs between normalization, performance, and usability, and document the reasoning behind them
• Reduce technical debt in the dbt project: model layering, naming and testing standards, macro reuse, and incremental strategy
• Own data quality and monitoring for the models you build:
• Define what "correct" means for each model and instrument data quality checks to monitor it
• Build and improve the alerting and triage process so data issues are caught proactively
• Lead root-cause investigations on data discrepancies across the pipeline, from source event or system of record through to the analytics layer
• Partner with Data Engineering and product engineering teams on upstream contracts and instrumentation quality
• Be the connective tissue between source data and business definitions:
• Support technical definition of core business measurement and drive alignment when stakeholders disagree
• Partner with Data Analysts and BI to ensure models enable stakeholder self-service analysis
• Translate business needs into technical requirements and prioritize work against them
• Raise the bar on the team through code review, design review, and documentation