Rocket Money

Design scalable dimensional data models in dbt and BigQuery, establish data quality standards, and translate business requirements into technical solutions. | Requires 5+ years in analytics or data engineering, deep dimensional modeling and star schema expertise, dbt and BI tool proficiency, and strong communication skills. | ABOUT ROCKET MONEY 🔮 Rocket Money’s mission is to empower people to live their best financial lives. 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. ABOUT THE TEAM 🤝 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. IN THIS ROLE, YOU'LL 🤹 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. You will: 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 ABOUT YOU 🦄 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. Bonus points if: You have worked with finance or financial services data — transactions, subscriptions, revenue recognition, ledgers, or reconciliation against a system of record. You have experience with our stack (BigQuery, dbt Cloud, Looker, Amplitude, DeepNote) You have experience with data governance, lineage, or catalog tooling You're familiar with Agile process & sprint cycles You have experience in mobile app or product analytics WE OFFER 💫 Health, Dental & Vision Plans Life Insurance Long/Short Term Disability Competitive Pay 401k Matching Team Member Stock Purchasing Program (TMSPP) Learning & Development Opportunities Tuition Reimbursement Unlimited PTO Daily Lunch, Snacks & Coffee (in-office only) Commuter benefits (in-office only) Additional information: Salary range of $150,000 - $190,000 + bonus + benefits. Base pay offered may vary depending on job-related knowledge, skills, and experience. Rocket Money, Inc. is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Build and evolve AI agentic behavior platform and collaborate cross-functionally to integrate capabilities into workflows. | Strong production software experience, deep understanding of LLM applications, agentic systems, and tool orchestration, with ability to design scalable platform abstractions. | 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. ABOUT THE TEAM 🤝 Rocket Money’s mission is to meaningfully improve the financial lives of millions of people. We help customers understand their money, reduce waste, stay on top of bills and subscriptions, and take action with more confidence. The Agents Group is building the agentic systems that let Rocket Money move from helpful product surfaces to assistants that can safely and reliably finish work for customers. The group owns the system responsible for enabling agentic capabilities and the methods that let agents interact with customers and systems across various contact methods (phone, email, web bots, etc) and autonomous workflows. This work sits at the intersection of LLM-powered reasoning, deterministic execution systems, tool orchestration, product constraints, observability, evaluation, trust and safety, and consumer-grade product craft. The bar is not “can the agent respond?” The bar is “can the agent correctly, safely, and verifiably complete the work?” ABOUT THE ROLE As an Applied AI Engineer, you will help build, evolve, and operate the Capabilities Engine: the core platform that makes agentic behavior reliable, reusable, observable, and production-ready across Rocket Money’s AI-powered experiences. You will create the primitives that capability teams need to turn ambiguous product needs into shippable technical plans, prototypes, and durable systems. You will also partner with contact-method owners across phone, email, chat, web login, ODC, and autonomous workflows to make each method more agentic and better integrated with the Capabilities Engine. That means helping agents plan, discover and invoke capabilities, recover from ambiguity, handle real-world edge cases, and complete user-facing work safely and correctly. This role is for an engineer who is excited by—and experienced in—AI as an execution layer, not just a conversation layer. You will build systems that let agents use tools, observe outcomes, recover from failure, and earn enough trust to act on behalf of users. You will work across product and platform boundaries, moving between architecture, implementation, debugging, evaluation, and cross-functional collaboration. Success means making the Capabilities Engine easier to extend, operate, and integrate with; improving task completion and recovery across methods; and ensuring new capabilities ship with clear contracts, tests, traces, ownership, and production-readiness expectations. IN THIS ROLE, YOU'LL: Build, evolve, and operate the Capabilities Engine as a core platform for agentic product behavior. Design abstractions that make capabilities easy for agents and methods to discover, invoke, compose, observe, and improve. Partner with method owners across all contact method subagents as well as supporting agents to make each method more agentic and better integrated with the Capabilities Engine. Identify repeated patterns across methods and turn them into reusable capabilities, interfaces, evaluations, and operating practices. Provide guidance on how to monitor and optimize reliability, latency, cost, observability, and safety of capability execution in production. Develop practical evaluation loops for agentic behavior, including offline tests, production metrics, traces, regression suites, and qualitative review. Debug complex failures across model behavior, tool execution, orchestration, product constraints, and downstream systems. Translate ambiguous product needs into shippable technical plans, prototypes, and durable systems. Create the primitives capability teams need to successfully produce shippable technical plans, prototypes, and durable systems. Collaborate with product, design, data, platform, and operations partners to ensure capabilities are useful, understandable, and safe for real users. Mentor and develop engineers across the Agents Group, acting as a technical multiplier by sharing expertise, fostering a culture where others feel empowered to propose ideas and take on high-impact work, and helping shape the group's overall technical direction and standards. Document system behavior, capability contracts, tradeoffs, and operational expectations so other teams can build on the platform confidently. Drop into high-priority method work when needed to accelerate agentic behavior, unblock launches, or stabilize production issues. Stay curious about the frontier of applied AI engineering and help the team adopt new tools, patterns, and workflows where they make the product more dependable. YOU MAY BE A GOOD FIT IF YOU: Have experience shipping production software with strong engineering fundamentals. Are excited by AI as an execution layer. You understand that durable customer value comes from orchestration, tools, workflows, evaluation, approvals, observability, and recovery. Have hands-on experience or strong practical familiarity with LLM applications, agentic systems, tool calling, retrieval, workflow orchestration, or applied AI products. Have strong product judgment. You can reason from customer need to product experience to technical architecture, and you know the difference between a clever demo and a dependable customer-facing system. Are comfortable working across ambiguity. You can take a broad problem, identify the crux, sequence the work, make tradeoffs, and keep moving without waiting for perfect clarity. Like building platform abstractions and shared services that help multiple teams or product surfaces move faster. Hold a high bar for quality, reliability, and trust. You know that systems acting on behalf of users need clear contracts, observability, evaluation, and recovery paths. Are energized by debugging messy production behavior across model outputs, distributed systems, product logic, user context, and downstream integrations. Communicate clearly with engineering, product, design, data, operations, and platform partners. Are motivated to actively mentor and grow others. Are a tenacious learner who stays engaged with the frontier of AI engineering without chasing every shiny object. Are motivated by measurable customer and operational impact, not just interesting technical demos. STRONG CANDIDATES MAY HAVE: Experience building production LLM applications, agentic systems, workflow engines, tool-calling systems, or AI-powered product surfaces. Experience designing capability platforms, shared services, orchestration systems, or reusable product infrastructure. Experience with TypeScript, Node, GraphQL, Postgres, LLM applications, agentic systems, and workflow orchestration. Experience creating evaluation and observability systems for probabilistic or agentic behavior. Experience improving reliability, alerting, tracing, incident response, operational quality, or other systems that make software safer and more dependable over time. Experience partnering deeply with Product, Design, Data, Operations, Legal, Support, or other cross-functional teams in a sensitive customer domain. Firsthand experience building products where customer trust, permissions, explainability, and recovery paths are central to the experience. A visible habit of turning repeated one-off work into reusable systems, patterns, and documentation. WE OFFER 💫 Health, Dental & Vision Plans Competitive Pay 401k Matching Unlimited PTO Lunch daily (in-office only) Snacks & Coffee (in-office only) Commuter benefits (in-office only) Additional information: Salary range of $200,000 - $270,000/year + bonus + benefits. Base pay offered may vary depending on job-related knowledge, skills, and experience. This job description is an outline of the primary responsibilities of this position and may be modified at the discretion of the company at any time. Decisions related to employment are not based on race, color, religion, national origin, sex, physical or mental disability, sexual orientation, gender identity or expression, age, military or veteran status or any other characteristic protected by state or federal law. The company provides reasonable accommodations to qualified individuals with disabilities in accordance with applicable state and federal laws. The information regarding compensation and other benefits included in this paragraph is the company’s current, good faith estimate at the time of posting. [Compensation and benefits are subject to modification from time to time as the Company, in its sole and exclusive discretion, deems appropriate.] The Company may determine during its future reviews of the proposed compensation and benefits provided for this position, that the compensation and benefits for such position should be reduced. In no event will the Company reduce the compensation for the position to a level below the applicable jurisdictional minimum wage rate for the position. Los Angeles County and San Francisco Candidates only: qualified applicants with arrest or conviction records will be considered for employment per the Fair Chance Ordinance and the Fair Chance Initiative for Hiring.