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6sense is seeking a Staff Software Engineer - Data to own large-scale data systems and drive technical direction across the organization. This is a senior individual contributor role for engineers who excel at owning complex systems end-to-end, influencing technical strategy across teams, and solving problems where correctness, scale, and reliability are critical.
Key responsibilities include designing and building high-throughput batch and streaming data pipelines that process billions of records, owning core datasets that power product features, analytics, and ML systems, and building systems for enrichment, deduplication, identity resolution, and confidence scoring at scale.
You will define long-term architecture for foundational data platforms, lead complex multi-quarter initiatives such as platform migrations and major refactors, and balance data correctness, freshness, cost, and performance in production systems. You'll own SLAs/SLOs for data freshness, accuracy, and completeness, design data quality frameworks and reconciliation mechanisms, and lead root-cause analysis for production issues.
Collaboration is essential—you'll partner with Product, Analytics, Data Science, Platform, and GTM teams to translate business needs into scalable data solutions, and act as a technical point of contact for third-party data integrations. You'll also mentor senior engineers, influence technical standards, lead by example in design reviews and incident response, and help shape engineering best practices around scalability, reliability, and maintainability.
Required qualifications: 10+ years building backend or data-intensive systems, proven ownership of mission-critical data platforms in production, and experience operating at Senior or Staff engineer scope. Strong programming skills in Python and SQL, deep experience with big-data and streaming systems (Spark, Kafka, Flink, Hive, Trino/Presto), and solid understanding of distributed systems and data consistency principles. Understanding of AI-native software development practices and proven ability to adopt modern engineering paradigms, particularly integrating AI into development workflows.
