What You'll Be Doing

• Design, build, and operate managed platform services for data storage, distributed query execution, batch and streaming processing, and workload orchestration.
• Own technical designs and implementation decisions for services and components within the broader platform architecture and lead delivery from requirements through deployment and support.
• Automate provisioning, configuration, upgrades, and monitoring for managed services to reduce manual operations and ensure consistency.
• Build and integrate metadata management tools including data catalogs and lineage tracking to help users discover datasets and understand their structure, ownership, and dependencies.
• Implement schema governance, data quality checks, access controls, and usage tracking aligned with policies, collaborating with security and data stakeholders.
• Work closely with users to diagnose integration and performance issues, clarify technical requirements, and improve platform services.
• Enhance reliability and performance of managed services through observability, capacity management, testing, production support, and incident response.
• Deliver platform upgrades and data migrations with proper validation and compatibility checks to protect downstream workloads.
• Lead design and code reviews for owned services, document technical trade-offs, and contribute to architectural improvements.
• Mentor less experienced engineers through collaboration and feedback, promoting maintainable code, automated testing, and operational best practices.

Requirements

• 7+ years of experience with modern data technologies and/or building data-intensive distributed systems.
• Expertise in Java and/or Python, with ability to deliver high-quality, maintainable production software and strong familiarity with SQL, Bash, and Linux environments.
• Experience building and operating managed data or infrastructure services including operational automation and production support.
• Demonstrated ownership of engineering projects or service components from design through deployment and operation.
• Strong understanding of distributed systems trade-offs including fault tolerance, concurrency, partitioning, and recovery from partial failures.
• Production experience with batch and streaming systems, including state management, retries, replay, and data consistency.
• Experience with data processing or query technologies such as Flink, Spark, or ClickHouse and ability to evaluate performance and operational trade-offs.
• Experience with multiple storage technologies: object storage, relational or NoSQL databases, event streaming systems like Kafka, and familiarity with open table formats such as Iceberg or Delta Lake.
• Practical experience with data modeling, schema governance and evolution, data quality controls, and integrating metadata management or lineage capabilities.
• Experience with data formats and serialization systems such as Arrow, Parquet, Protobuf, or Avro.
• Experience with cloud-native data infrastructure including Kubernetes, workflow orchestration, automated deployment, and production monitoring.
• Strong communication and problem-solving skills with ability to troubleshoot, collaborate cross-functionally, prioritize work, and deliver in fast-paced environments.

Nice to Haves

• Experience extending or scaling data catalogs or lineage tracking tools across multiple storage and compute technologies.
• Experience implementing access controls, usage tracking, or other governance capabilities in shared data environments.
• Familiarity with financial datasets and the needs of trading, quantitative research, or back-office users.

Perks and Benefits

• Annual base salary range of $160,000 to $250,000 depending on experience and qualifications.
• Eligibility for annual discretionary bonus.
• Comprehensive benefits package including medical, pharmacy, dental, and vision insurance.
• 401k plan with discretionary employer match.
• Short and long-term disability, life and AD&D insurance.
• Health savings accounts and flexible spending accounts.