Senior Data Engineer

Valon is transforming mortgage servicing and consumer direct lending with a technology-first approach. The Senior Data Engineer will manage and optimize the core data infrastructure, build custom data ingestion pipelines, and collaborate with the analytics team to architect the optimal long-term infrastructure strategy.

Responsibilities:

• Own and optimize Valon’s core data infrastructure for performance, reliability, and cost efficiency as data volume and complexity grow
• Design and extend multi-tenant data architectures to support a scalable SaaS model
• Evaluate and improve warehouse design, data models, and schema strategies to minimize redundancy and maximize query performance
• Lead and execute infrastructure migrations with minimal downtime and high data integrity
• Build and maintain custom ingestion pipelines for data sources not covered by ELT vendors
• Build or integrate observability tooling for pipeline health, latency, data freshness, drift detection, and schema change monitoring
• Optimize data transformation jobs for speed and cost efficiency, identifying performance bottlenecks and refactoring as needed
• Establish and run routine maintenance processes to ensure data quality, stability, and platform consistency
• Drive a culture of data reliability engineering by codifying best practices, automation, and operational excellence
• Define and communicate the long-term data infrastructure roadmap, aligning technical evolution with Valon’s business growth
• Think strategically: What does our infrastructure need to look like at 10x or 100x scale?
• Partner closely with the analytics team to enable faster insights through better tooling, performance, and documentation
• Act as a force multiplier for the broader data team - mentoring on best practices and improving development velocity across pipelines and models
• Collaborate with platform and product teams to design systems that meet business, operational, and regulatory requirements
• Occasionally take on complex, non-infrastructure data projects, such as data products or analytical initiatives, to stay close to end-user needs and maintain empathy with analysts

Requirements:

• 4+ years of data engineering experience successfully creating/optimizing/scaling data stacks and supporting data teams, at least 2 years in an early- to mid-stage startup
• Deep knowledge and experience deploying and optimizing dbt or comparable tooling
• Experience managing multiple types of data management systems (data warehouses, data marts, data lakes)
• Experience building/procuring tools and processes to monitor end-to-end data stack health
• Mastery of SQL from both functionality and performance perspective
• Fluency in writing scripts in Python or a similar language
• Experience working with and tuning cloud data warehouses and compute environments
• Experience optimizing BI tool performance and managing permissions
• Experience managing/tuning EL connector solutions (such as Fivetran, Stitch, etc.) and building custom ingestion pipelines
• Strong prioritization skills, effort vs. impact thinking, stakeholder management
• Ability to reason about factors/interactions/root causes outcomes across the data stack, able to back up arguments/decisions with data
• Deep understanding of data team workstreams and needs, ideally in the form of past experience as a data analyst/data scientist
• Experience with BigQuery/GCP a plus
• Looker experience a plus