Staff Software Engineer - Payroll Data

What you will do

• Architect and Build Core Data Systems: Design and implement scalable data infrastructure and pipelines that power payroll reports, pay stubs, tax and accounting integrations, billing, and AI use cases.
• Define the Unified Query Layer: Partner with other Staff and Principal engineers to design and evolve a cohesive data query layer that spans batch and real-time processing, reducing fragmentation and enabling consistent access patterns.
• Own End-to-End Data Pipelines: Build and maintain ETL and streaming pipelines, owning data correctness, schema evolution, backfills, and lifecycle management across upstream and downstream systems.
• Drive Non-Functional Excellence: Lead by example in building systems that are observable, debuggable, scalable, and reliable. Set standards for monitoring, logging, alerting, and data quality checks.
• Solve Deep Interoperability Problems: Tackle complex challenges at the boundaries between Payroll Data and other core systems (Object Graph, Reporting, RQL, AI), ensuring clean abstractions and long-term extensibility.
• Technical Leadership & Mentorship: Act as a technical multiplier—review designs, unblock other engineers, mentor senior engineers, and raise the overall technical bar of the team.
• Partner Cross-Functionally: Work closely with Product, Platform, AI, and other Payroll teams to translate product needs into robust platform capabilities while influencing roadmaps with data-informed tradeoffs.

What you will need

• 8+ years of professional software engineering experience, with demonstrated impact on complex backend or data-intensive systems.
• Strong data infrastructure background, including experience with data pipelines, ETL/ELT systems, or large-scale data platforms.
• Expertise in distributed systems, with an understanding of tradeoffs across batch vs. real-time processing, data materialization, and aggregation strategies.
• Proficiency in a modern backend language (e.g., Go, Java, Python) and comfort working across multiple systems and services.
• Experience with transactional and analytical data stores, and optimizing schemas and queries for correctness and performance.
• Platform mindset, with a track record of building reusable abstractions and primitives consumed by many teams or products.
• Operational rigor, including hands-on experience designing for observability, reliability, and debuggability in production systems.
• Strong communication skills, with the ability to clearly articulate complex technical concepts and influence design decisions across teams.
• Ownership and autonomy, balanced with collaboration—comfortable leading large initiatives while partnering effectively with others.