AI agents that automate reconciliations, close workflows, and journal entries — fully auditable, human-approved every step. Trusted by 3,500+ teams worldwide. Backed by Insight, Norwest and ICONIQ.
About the role
As a Senior Staff Engineer, Data, you'll be a key technical leader driving the design, implementation, and evolution of FloQast's core data platform. You will define the standards and patterns that power data ingestion, governance, storage, processing, and access across all product and analytics systems — with Apache Spark as the primary compute engine at the heart of that stack.
What they're looking for
- Data Platform Architecture: Lead the design of scalable and reliable data pipelines, storage solutions, and access layers using modern cloud-native technologies
- Foundational Systems: Own and evolve the core data platform components (e.g., event streaming, lakehouse architecture, batch & streaming ETL, data cataloging)
- Data Governance: Define best practices for data quality, lineage, privacy, and access control to ensure regulatory compliance and trust in the data
- Cross-functional Collaboration: Work closely with Product, Analytics, Infrastructure, and Security teams to align data platform capabilities with organizational goals
- Technical Leadership: Mentor engineers across the organization, establish coding and architectural standards, and influence the strategic direction of the platform
- Innovation & Modernization: Evaluate and integrate emerging technologies that improve performance, observability, developer experience, and cost efficiency
More about this role
As a Senior Staff Engineer, Data, you'll be a key technical leader driving the design, implementation, and evolution of FloQast's core data platform. You will define the standards and patterns that power data ingestion, governance, storage, processing, and access across all product and analytics systems — with Apache Spark as the primary compute engine at the heart of that stack. Your work will enable teams across engineering, product, and business operations to build on a reliable, scalable, and secure data foundation.
You've spent years going deep on Spark in production. You reason through shuffle behavior, partition strategies, and memory pressure without reaching for documentation. You understand what the Catalyst optimizer does with your query plan and you write code that helps it. You've made the call between PySpark and Scala Spark on real workloads, run Structured Streaming pipelines over Kafka topics on MSK, and debugged slow stages in the Spark UI. You've built Spark jobs that read and write Apache Iceberg tables at scale — managing snapshot isolation, schema evolution, and compaction as operational concerns, not afterthoughts. At this level, you don't just tune...
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