Principal Engineer - Open Source Data Platform (ODP)

Define and drive the long-term technical strategy and architecture for the Open Data Platform, aligning with business objectives and industry trends.

Own the design of the most complex, high-impact systems and establish architectural principles and patterns that scale across the organization.

Identify emerging technologies and industry trends; lead research and development initiatives that position Acceldata at the cutting edge of data platform innovation.

Serve as a recognized leader in the open-source community; drive Apache project contributions, represent Acceldata at conferences, and influence project roadmaps.

Collaborate with CTO, VP of Engineering, and Product leadership to translate business strategy into technical execution; provide technical due diligence for strategic initiatives.

Influence engineering practices, tools, and culture across multiple teams; establish best practices that elevate the entire engineering organization.

Mentor Staff Engineers and Senior Engineers; develop technical leadership capabilities across the organization.

Lead resolution of the most challenging technical problems spanning architecture, performance, scalability, and reliability.

Engage with strategic customers and partners on complex technical discussions; translate customer needs into platform capabilities.

Drive alignment across engineering, product, and operations on technical decisions with broad organizational impact.

Work across diverse environments: Bare Metals, VM, Kubernetes, multi-cloud, and hybrid architectures at enterprise scale.

15+ years of hands-on software development experience with at least 8 years focused on distributed systems, big data platforms, or data infrastructure.

Proven track record of leading large-scale technical initiatives from conception to production across multiple teams.

Expert-level proficiency in Java or Scala; strong skills in Python and systems languages.

Deep expertise in distributed computing, including consensus protocols, distributed transactions, data replication, partitioning strategies, and optimization with modern table formats.

Extensive experience in architecting and scaling systems using Hadoop, Spark, Hive, Trino, Kafka, Flink, and related technologies at production scale (100s to 1000s of nodes).

Demonstrated ability to design and evolve complex systems that handle petabyte-scale data with high availability and performance requirements.

Expert knowledge of cloud-native architectures, Kubernetes orchestration, and multi-cloud deployment patterns.

Track record of diagnosing and resolving complex distributed system issues, including performance optimization, resource management, and failure mode analysis.

Significant contributions to major open-source projects; experience working with distributed global teams and open-source governance models.

Exceptional written and verbal communication skills; proven ability to influence technical direction across organizations and with external stakeholders.

Ability to balance long-term technical vision with near-term delivery requirements; experience making build vs. buy decisions.

PMC member or committer status in Apache projects (e.g., Spark, Kafka, Hive, Hadoop, Iceberg, Flink, Trino).

Speaker at major conferences (ApacheCon, Spark Summit, Kafka Summit, QCon, etc.); published papers or widely-read technical content.

Experience building or contributing to query engines, optimizers, or execution frameworks.

Deep experience with modern lakehouse architectures, table formats (Iceberg, Delta, Hudi), and data mesh patterns.

Experience with ML infrastructure, feature stores, or MLOps platforms.

Experience scaling engineering organisations in high-growth environments.

Master's or PhD in Computer Science, with a focus on distributed systems, databases, or related fields.