About the Team

This team designs intelligent, autonomous systems that eliminate financial operations for companies globally. We automate everything from bookkeeping and reconciliation to tax, payroll, forecasting, and document understanding: turning hours of work into seconds. The team blends deep research pedigree with proven industry experience. We operate with high standards, are tool-agnostic, and expect engineers to own problems end-to-end. We collaborate closely, push boundaries responsibly, and ship exceptional work quickly. You'll solve unprecedented technical challenges using unique, end-to-end financial datasets no one else in the industry has.

About the Role

As a Senior Manager of Data Platform Engineering, you'll lead the team responsible for the data backbone of our financial systems. You'll set the technical direction for high-volume financial data ingestion, our Databricks lakehouse, data orchestration, canonical financial models, and the infrastructure that makes our data platform reliable, scalable, and increasingly real-time.

You'll combine strong technical judgment with people leadership-building a high-performing engineering organization, establishing platform standards, driving major migrations, and partnering closely with product, accounting, and platform teams. You'll help shape a data platform where correctness, auditability, performance, and operational excellence are foundational.

This role is based in San Francisco.

What You'll Do

• Lead and grow a high-performing data platform engineering team, providing technical direction, mentorship, career development, and clear ownership.
• Set the technical strategy for high-volume financial data ingestion across payment processors, banks, billing systems, and ERP/GL platforms, with strong guarantees around correctness, replayability, and recovery.
• Lead the evolution of our Databricks lakehouse using Spark, Delta Lake, Unity Catalog, dbt, Python, and SQL, optimizing for scale, performance, reliability, and cost.
• Define standards for data contracts, schema evolution, drift detection, data quality, and canonical financial models.
• Evolve the platform from batch toward near-real-time processing using Kafka, CDC, micro-batching, incremental processing, and Dagster orchestration.
• Own the architecture of canonical financial models that transform complex source data into consistent financial objects and relationships powering our accounting engine.
• Partner with infrastructure and platform teams to build reliable production foundations across observability, alerting, incident response, recovery, and operational readiness.
• Guide infrastructure strategy across GCP, Terraform, Kubernetes/GKE, Helm, GitOps, and CI/CD.
• Lead migrations from legacy pipelines to the lakehouse, with rigorous parity validation, staged rollouts, and clear rollback strategies.
• Drive performance and cost optimization across PostgreSQL, Databricks, and large-scale data workloads.
• Partner with product, accounting, ML, and platform leadership to translate business needs into scalable data platform capabilities.
• Set technical direction through architecture reviews, design documents, engineering standards, and long-term platform roadmaps.
• Champion AI-assisted engineering practices that improve team velocity while maintaining high standards for code quality, testing, security, and reliability.

What You'll Need to Have

• 8+ years of experience in data platform, backend, or infrastructure engineering, including significant experience leading engineering teams.
• Proven experience managing and developing senior engineers and technical leads and building strong, autonomous teams.
• Deep expertise in data platforms, distributed systems, data modeling, and production data pipelines.
• Strong experience with Python and SQL, plus hands-on experience with Spark, Databricks, Delta Lake, dbt, and modern lakehouse architectures.
• Experience building reliable ingestion systems with large-scale backfills, incremental processing, checkpointing, rate-limit handling, idempotency, and failure recovery.
• Strong understanding of streaming and event-driven architectures, including Kafka, CDC, and micro-batching.
• Experience with data orchestration platforms such as Dagster and designing for freshness, partitioning, and incremental computation.
• Experience optimizing PostgreSQL and analytical workloads for performance and cost.
• Experience operating production infrastructure on GCP, with familiarity with Terraform, Kubernetes/GKE, Helm, GitOps, and CI/CD.
• Strong understanding of observability, reliability engineering, and production operations.
• Excellent technical communication skills, with the ability to influence architecture and engineering decisions across teams.
• Experience leading complex technical migrations and balancing long-term platform strategy with near-term business priorities.
• Passion for building teams and infrastructure that power reliable, scalable, mission-critical financial systems.

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