Valon Mortgage - Senior Analytics Engineer, VM
Requirements
• Experience: 7+ years of combined experience in data engineering, analytics engineering, or a highly technical data analytics role, ideally at a mortgage servicer 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. • Bachelor’s Degree or equivalent professional experience in Computer Science, Data Analytics, or a similar field.
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 dbt Cloud 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 (e.g., Great Expectations) 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. • Compliance & Reporting: Own and fulfill third-party audit, exam, and regulatory reporting requirements, managing timelines and delivering clear, defensible outputs to clients and regulators.
Benefits
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