Senior Analytics Engineer

Rocket Money•Washington, DC

11d•$150,000 - $190,000

About The Position

Rocket Money’s mission is to empower people to live their best financial lives by providing members with a clear understanding of their finances and services that save them time and money. The Analytics Engineering team makes data consistent, trustworthy, and easy to use. It builds dimensional models that power business decisions and owns the integrity of the data behind those decisions. Analytics Engineers work closely with the Data Analyst teams they support while remaining part of a central Analytics Engineering practice. They translate business questions into technical requirements, model ingested source data for business use, and partner with engineering teams on upstream data. The Senior Analytics Engineer will help shape the team’s growing data architecture and build and iterate on the star schema. The role will surface models that Data Analysts and stakeholders can confidently use for self-service, while improving data literacy across the company. The person in this role will serve as a builder, architect, and consultant for analytics data, learning source data and transformations, monitoring integrity, and strengthening partnerships to maximize business value.

Requirements

At least 5 years of experience in analytics engineering, data engineering, or related roles.
Deep data modeling expertise, including designing dimensional models from scratch and integrating them into a cohesive analytics architecture.
Understanding of grain, slowly changing dimension (SCD) types, and fact table design tradeoffs.
Ability to handle complex modeling scenarios, including conforming dimensions across business use cases, many-to-many relationships, late-arriving and restated data, and evolving source systems.
Experience building data quality testing and monitoring into pipelines, defining SLAs or expectations with data owners, and managing on-call or triage processes.
Production dbt experience, including model materialization strategies, testing, macros, and maintaining a growing project.
At least 2 years of experience with BI tools and an understanding of how modeling choices affect analytics capabilities.
Strong cross-functional communication skills and ability to influence stakeholder and business decisions.
Strong problem decomposition skills, including identifying the underlying problem in requests and aligning them with the modeling roadmap and architecture.

Nice To Haves

Experience with finance or financial services data, such as transactions, subscriptions, revenue recognition, ledgers, or reconciliation against a system of record.
Experience with BigQuery, dbt Cloud, Looker, Amplitude, or DeepNote.
Experience with data governance, lineage, or catalog tooling.
Familiarity with Agile processes and sprint cycles.
Experience in mobile app or product analytics.

Responsibilities

Design and build scalable dimensional models using dbt and BigQuery.
Translate ambiguous business questions into dimensions, facts, and grain decisions that remain useful as the business changes.
Balance normalization, performance, and usability, and document the rationale for modeling decisions.
Reduce technical debt in the dbt project, including improving model layering, naming and testing standards, macro reuse, and incremental strategies.
Own data quality and monitoring for the models built.
Define correctness for each model and implement data quality checks.
Build and improve alerting and triage processes so data issues are identified proactively.
Lead root-cause investigations into data discrepancies across the pipeline, from source events or systems of record through the analytics layer.
Partner with Data Engineering and product engineering teams on upstream data contracts and instrumentation quality.
Connect source data with business definitions.
Support the technical definition of core business measurements and drive alignment when stakeholders disagree.
Partner with Data Analysts and BI to ensure models support stakeholder self-service analysis.
Translate business needs into technical requirements and prioritize work against them.
Raise team standards through code reviews, design reviews, and documentation.