The Role
The Staff Engineer will lead the architecture and technical vision for the Acceldata Open Data Platform, focusing on distributed systems, mentoring engineers, and contributing to open-source communities.
Summary Generated by Built In
ABOUT THE ROLE
WHY JOIN US
We are seeking a Staff Engineer with at least 9 years of experience in distributed systems or data platform development. In this role, you will be a technical leader responsible for architecting and driving the technical direction of critical components within the Acceldata Open Data Platform (ODP). You will work on solving the most challenging distributed systems problems, contribute to open-source communities, and mentor senior engineers while shaping the future of large-scale data infrastructure.
You'll lead high-impact initiatives that directly influence how petabyte-scale data is managed, observed, and optimized across hybrid and cloud ecosystems. You'll collaborate with industry experts, Apache committers, and seasoned engineers who share your passion for distributed systems and open-source innovation.
WHY JOIN US
At Acceldata, you won't just be writing code; you'll be defining the architecture and technical vision for modern data platforms used by some of the world's largest enterprises.
Your technical decisions will shape mission-critical systems, and your contributions will be visible across the global data ecosystem. If you're ready to take ownership of complex technical challenges and drive innovation at scale, Acceldata is where you belong.
RESPONSIBILITIES
- Own the end-to-end architecture of major platform components, driving design decisions that impact scalability, reliability, and performance.
- Architect and implement next-generation distributed data storage, processing, and query optimization systems.
- Partner with product, engineering leadership, and stakeholders to define technical roadmaps and translate business requirements into scalable solutions.
- Lead contributions to Apache projects and open-source communities, establishing Acceldata's presence and technical reputation.
- Identify and resolve complex performance bottlenecks across distributed systems, optimizing for cost, latency, and throughput.
- Mentor and guide Senior Software Engineers, fostering technical excellence and a culture of continuous learning.
- Establish and enforce coding standards, perform thorough code reviews, and ensure architectural consistency across the platform.
- Evaluate and recommend emerging technologies, tools, and frameworks that align with platform goals.
- Lead root cause analysis for critical production issues and drive systemic improvements to prevent recurrence.
- Work across diverse environments: Bare Metals, VM, Kubernetes, and multi-cloud architectures.
REQUIREMENTS
- 9+ years of hands-on software development experience with at least 5 years focused on distributed systems or big data platforms.
- Expert-level proficiency in Java or Scala, with strong Python skills. Experience with systems languages is a plus.
- Deep understanding of distributed computing principles, including consensus algorithms, data partitioning, replication, fault tolerance, and consistency models.
- Extensive hands-on experience with multiple components of the big data stack (Hadoop, Spark, Hive, Trino, Kafka, Flink, NiFi or similar).
- Expert-level Linux proficiency, including kernel tuning, performance analysis, and debugging at the system level.
- Strong experience with Kubernetes, Docker, and cloud platforms (AWS, GCP, or Azure).
- Deep expertise with Maven, Gradle, or SBT; experienced in CI/CD pipelines, GitHub, and artifact management (Nexus).
- Proven ability to diagnose and resolve complex issues, including memory leaks, deadlocks, GC tuning, and distributed system failures.
- Strong understanding of architectural patterns for scalable, fault-tolerant distributed systems.
- Excellent written and verbal communication skills; ability to articulate complex technical concepts to diverse audiences, including senior leadership.
- Demonstrated ability to work effectively with cross-functional teams, external contributors, and the open-source community. PREFERRED SKILLS
- History of significant contributions or committer status in Apache projects (e.g., Spark, Kafka, Hive, Iceberg, Flink, etc.).
- Experience with modern table formats (Apache Iceberg, Delta Lake, Hudi) and data lake architectures.
- Experience developing or optimizing query engines, query planners, or execution frameworks.
- Familiarity with data governance, security, lineage, and compliance frameworks.
- Experience leading large-scale data platform migrations or Hadoop distribution upgrades.
- Published technical blogs, papers, or conference presentations on distributed systems topics.
- Experience leading or maintaining open-source projects.
Skills Required
- 9+ years of hands-on software development experience
- 5 years focused on distributed systems or big data platforms
- Expert-level proficiency in Java or Scala
- Strong Python skills
- Deep understanding of distributed computing principles
- Extensive hands-on experience with components of the big data stack
- Expert-level Linux proficiency
- Strong experience with Kubernetes, Docker, and cloud platforms
- Deep expertise with Maven, Gradle, or SBT
- Excellent written and verbal communication skills
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
Founded in 2018, Campbell, CA-based Acceldata has developed the world's first enterprise data observability platform to help enterprises build and operate great data products. Acceldata's solutions have been embraced by global customers, such as Dun & Bradstreet, Verisk, Oracle, PubMatic, PhonePe (Walmart), and many more. Acceldata investors include Insight Partners, March Capital, Industry Ventures, Lightspeed, Sorenson Ventures, Sanabil, and Emergent Ventures. Contact us to learn about the benefits of data observability.
