Senior Analytics Engineer

About the Company

Valon is the leading AI platform for mortgage servicing, building the AI-native operating system for regulated finance.

Before providing the system of record that powers the entire industry, we built and ran our own mortgage servicer on it. That gave us a real regulated environment to prove our technology could replace the mainframe systems servicers have used for decades. Carrington acquired that servicer and adopted ValonOS as its core platform, allowing us to focus entirely on scaling our SaaS platform.

ValonOS is now on track to power 1 in 6 U.S. mortgages across major mortgage servicers like Newrez, Carrington, and ServiceMac. Every process on the platform is structured and programmable, enabling our AI agents to perform core servicing work, like handling payments, escrow, and homeowner questions, directly within the system's controls.

We recently raised a $150 million Series D at a $2.3 billion valuation by new investor Ribbit Capital, with continued support from existing backer a16z, amongst others.

About the Role

We are seeking a Senior Analytics Engineer to take ownership of our client reporting data models and pipelines. This crucial role involves managing the layer of our data models responsible for transforming raw data into reliable, structured, and performant assets for reporting, analysis, and AI initiatives. This role sits at the intersection of analytics and data engineering, ensuring data is production-grade, auditable, and easily accessible across the organization and our customers.

You will own client reporting, standardize our data modeling practices, manage the orchestration of analytical workflows, and provide hands-on analytical support for high-stakes operational business functions, including reporting and performance management.

Responsibilities

• Client Reporting & Infrastructure: Scope and develop new client reports, while also maintaining and enhancing existing reporting pipelines. Collaborate with external clients and our internal client success team to effectively manage and triage inbound requests. Partner with Valon Technologies' data team to improve the underlying infrastructure for client reporting.
• Data Modeling & Transformation: Design, build, and maintain the production-grade data models and analytical layers using our existing stack of BigQuery, dbt, and Airflow to ensure reliability and structure at scale.
• Data Governance & Quality: Partner with engineering and product teams to define and implement data contracts and observability frameworks to guarantee data quality and auditability for a regulated environment.
• Analytical Support: Write complex
• SQL queries
• , build dashboards (in tools like Hex or Looker), and conduct ad hoc analysis to empower operational leaders and provide deep root-cause insights into business performance.
• Ideal Background & Qualifications
• Experience: 7+ years of combined experience in data engineering, analytics engineering, or a highly technical data analytics role, ideally at a technology company or financial institution (Series B–E startup experience is a plus).
• Technical Stack: Expertise in SQL
• (required), strong proficiency in Python
• , and proven experience with core modern data stack tools like dbt and
• Airflow.
• Communication: Communicate effectively with external clients, clearly synthesizing their requests, establishing realistic timelines, and proactively managing expectations, including pushing back when necessary to ensure successful outcomes.
• Architecture & Quality: Deep understanding of data architecture patterns (e.g. medallion architecture) and experience implementing data quality frameworks, observability tools, or data contracts at scale.
• Analytical Tools: Experience with data visualization tools (Hex, Looker, or equivalent) and proficiency in Google Sheets/Microsoft Excel.
• Mindset: A strong product mindset, focusing on the usability, reliability, and developer experience of the data systems you build.
• Problem-Solving: Proven ability to solve complex problems independently and build repeatable processes around novel situations.

Minimum Qualifications

• Bachelor’s Degree or equivalent professional experience in Computer Science, Data Analytics, or a similar field.t professional experience in Computer Science, Data Analytics, or a similar field.