Staff Software Engineer I

Confluent6 days ago

Location

*Job Posting Only: USA1

Workplace

Remote

Type

Full Time

Salary

USD 261,300 – 307,000

Level

Staff

Role

Staff Engineer

Posted

Jun 23, 2026

Full TimeRemoteStaff

The role

Summary

Staff Software Engineer I at Confluent will design and implement innovative strategies for table materialization, compaction, and multi-tenant infrastructure as part of the Tableflow project, advancing Kora's multi-modal storage engine. This role requires deep expertise in distributed systems, storage engines, and platform engineering to handle streaming data at massive scale across Confluent Cloud. The ideal candidate brings a strong computer science foundation, open-source contributions (Kafka, Iceberg, Flink), and proven ability to influence stakeholders while architecting highly available, scalable infrastructure.

What you'll do

Storage Engine Architecture & Implementation: Design and implement innovative strategies for table schematization, materialization, and compaction at massive scale across Confluent Cloud. Architect efficient data structures and algorithms that balance performance, cost, and reliability for stream-table duality processing.

Multi-Tenant Infrastructure Engineering: Build and maintain highly available, resilient multi-tenant compute infrastructure powering Kora's background computational tasks. Ensure platform optimization for scalability, reliability, and resource efficiency while managing infrastructure across distributed cloud environments.

Tableflow Project Leadership: Advance key layers of the Tableflow project, focusing on core components responsible for table materialization and background maintenance. Drive technical decisions and implementation strategies for transforming Kora into a true multi-modal storage engine supporting seamless stream and table consumption.

Cross-Functional Technical Collaboration: Partner with product management, design, and engineering teams across the organization to ensure seamless integration of storage features and infrastructure components. Communicate complex technical concepts to non-technical stakeholders and guide architectural decisions that align with organizational objectives.

Distributed Systems Problem Solving: Tackle complex distributed storage systems challenges including high availability strategies, scalability patterns, and maintenance efficiency for massive-scale data processing. Develop and implement solutions for materialization and maintenance challenges specific to streaming data platforms.

What we look for

Technical

Distributed Systems DesignDeep expertise in designing and implementing distributed storage systems, data infrastructure, and multi-tenant architectures capable of handling massive scale data processing with high availability and consistency guarantees.

Stream Processing & Data SystemsStrong understanding of streaming data platforms, stream-table duality concepts, and experience with materializing views or maintaining derived data structures at scale in distributed environments.

Database & Storage Engine FundamentalsComprehensive knowledge of database internals, storage engines, table materialization techniques, compaction strategies, and indexing approaches for optimizing query performance and storage efficiency.

Cloud Infrastructure & DevOpsDemonstrated proficiency with cloud platforms (AWS, Azure, or GCP), containerization, orchestration, and infrastructure-as-code practices for building scalable, resilient systems in production environments.

Performance Optimization & ScalabilityProven track record optimizing systems for throughput, latency, and resource efficiency at scale. Experience identifying bottlenecks, designing horizontal scaling strategies, and implementing high-performance data processing pipelines.

Education

Computer Science or Related FieldBachelor's, Master's, or PhD degree in Computer Science, Computer Engineering, Software Engineering, or equivalent field. Equivalent professional experience in systems engineering or distributed systems development is acceptable as an alternative.

Experience

Senior-Level Systems Engineering (8+ years)Minimum 8+ years of professional software engineering experience with substantial time spent designing and implementing distributed systems, data infrastructure, or storage solutions. Demonstrated progression to senior technical leadership roles.

Storage & Infrastructure ProjectsProven experience building production storage systems, data platforms, or infrastructure components handling massive scale. Track record of shipping features that improved system performance, reliability, or scalability in distributed environments.

Open-Source ContributionsActive contributions to open-source data systems projects, particularly Apache Kafka, Apache Iceberg, or Apache Flink. Demonstrated understanding of streaming data platforms and their architectural patterns through community involvement.

Stakeholder Leadership & CommunicationExperience influencing and guiding technical decisions across multiple teams and organizational levels. Proven ability to communicate complex technical concepts to diverse audiences including product, design, and executive stakeholders.

Skills

Required skills

Distributed Systems ArchitectureExpert-level design and implementation of distributed systems with expertise in consensus protocols, replication strategies, fault tolerance, and consistency models. Ability to architect systems that scale horizontally while maintaining reliability.

Java or Scala ProgrammingAdvanced proficiency in Java or Scala for building production systems. Deep understanding of JVM performance characteristics, garbage collection, memory management, and concurrent programming patterns essential for high-throughput data systems.

Data Systems EngineeringStrong expertise in designing data systems including query optimization, indexing strategies, data serialization formats, and schema evolution. Understanding of columnar formats, compression techniques, and materialization patterns.

Database InternalsComprehensive knowledge of relational and columnar database engines, query planning, execution optimization, ACID properties, transactions, and distributed query processing. Familiarity with modern data warehouse and lakehouse architectures.

Cloud Platform OperationsProduction experience with AWS, Azure, or GCP. Proficiency in services like EC2, RDS, S3, Kubernetes, load balancing, monitoring, and infrastructure automation for deploying and managing large-scale systems.

Nice to have

Apache Kafka ExpertiseDeep hands-on experience or open-source contributions to Apache Kafka. Understanding of Kafka's architecture, replication, topic design, and integration patterns for event streaming platforms. Familiarity with Confluent's ecosystem and extensions.

Apache Iceberg KnowledgeExperience with Apache Iceberg table format, including schema evolution, partitioning strategies, hidden partitions, and time-travel queries. Understanding of how Iceberg solves data reliability and query optimization challenges in data lakes.

Apache Flink ProficiencyHands-on experience with Apache Flink for stream processing, including stateful computations, windowing, checkpointing, and distributed state management. Understanding of Flink's execution model and optimization techniques.

Stream-Table DualityConceptual and practical understanding of stream-table duality and how streaming and batch perspectives can be unified. Experience implementing dual-mode systems that provide consistent semantics across stream and batch processing.

Performance Benchmarking & ProfilingExpertise in profiling distributed systems, identifying performance bottlenecks, and conducting rigorous benchmarking studies. Experience with tools like Java Flight Recorder, flame graphs, and continuous performance monitoring in production.

Kubernetes & Container OrchestrationProduction experience with Kubernetes for deploying, scaling, and managing containerized data systems. Proficiency with helm charts, custom resources, and orchestration patterns for complex multi-tenant architectures.

Compensation & benefits

Salary

USD 261,300 – 307,000 (annual)

Stock options

Available

Benefits

Comprehensive Health Coverage

Medical, dental, and vision insurance with plans covering employees, dependents, and qualifying family members. Access to preventive care, specialist networks, and prescription drug coverage.

Competitive Retirement Plans

401(k) retirement savings plan with company matching contributions to help build long-term financial security and retirement readiness.

Generous Time Off

Unlimited paid time off policy allowing flexibility for vacation, personal wellness, and life events. Additional company holidays and paid leave for specific circumstances.

Equity & Stock Options

Participate in company success through stock options and equity grants vesting over time. Opportunity to benefit from company growth as a technical leader.

Professional Development

Learning budgets for conferences, courses, and certifications. Access to mentorship programs, internal technical workshops, and opportunities to present at industry events.

Workplace Flexibility

Remote work options and flexible scheduling to balance work and personal life. Distributed team structure respecting diverse time zones and work preferences.

Wellness Programs

Gym memberships, mental health resources, meditation apps, and wellness initiatives supporting employee physical and mental well-being.

Parental & Family Support

Paid parental leave for primary and secondary caregivers, adoption assistance, and family planning benefits supporting employees at all life stages.

Interview process

1
Initial Screening Call — Recruiter conversation to understand background, career trajectory, and motivation for the role. Discussion of technical interests, experience with distributed systems, and alignment with Confluent's culture and mission.
2
Technical Phone Interview — Focused discussion on distributed systems design principles, data platform architecture, and problem-solving approach. Potential discussion of Apache Kafka, Iceberg, or Flink experience. No live coding required at this stage.
3
Architecture Design Discussion — Deep-dive session exploring how you would design key components of the Tableflow system or similar distributed infrastructure challenges. Discussion of tradeoffs, scalability considerations, and production deployment strategies.
4
System Design Interview — Collaborative problem-solving session designing a large-scale distributed system from first principles. Focus on handling massive data volumes, multi-tenancy, high availability, and operational concerns at production scale.
5
Stakeholder Impact Conversation — Discussion with team members about cross-functional collaboration, communication approach, and experience influencing technical decisions. Questions about working with product, design, and other engineering teams.
6
Leadership & Culture Fit Discussion — Conversation with senior leaders or hiring manager exploring technical vision, leadership philosophy, and values alignment with Confluent's collaborative, inclusive culture and commitment to engineering excellence.