Core Responsibilities
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Design, build, and optimize high-throughput data processing systems (both real-time streaming and batch).
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Engineer robust storage solutions capable of handling rapidly expanding volumes of time-series and spatial data without compromising on query performance.
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Drive architectural decisions to ensure the infrastructure remains fault-tolerant and ahead of the company's aggressive scaling trajectory.
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Develop intelligent, automated tooling that improves data quality, lineage tracking, and system reliability.
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Collaborate closely with internal stakeholders to define technical abstractions that can be reused across different product lines.
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Write scalable, production-ready code, primarily utilizing Java and Python.
Requirements
Candidate Profile
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Scale Experience: A proven track record of designing and maintaining distributed backend systems or data platforms that process data at the terabyte or petabyte scale.
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Technical Foundation: Deep expertise in computer science fundamentals, system architecture, and anticipating failure modes in complex networks.
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Language Proficiency: Advanced proficiency in a JVM language (Java, Scala, Kotlin) and a willingness to work across different stacks as needed.
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Data Ecosystems: Hands-on experience with modern large-scale processing frameworks (e.g., event streaming, distributed computation, and advanced data warehousing/lakehouse concepts).
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Autonomy: High comfort level navigating ambiguity. You know how to scope complex problems, make definitive architectural calls, and drive projects to completion independently.
Bonus Points
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Active contributions to open-source software, particularly in the distributed systems or data infrastructure space.
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Familiarity with modern orchestration engines, lakehouse architectures, or spatio-temporal data models.
Benefits
Work Environment
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High Autonomy: Engineers own their domains end-to-end and have a direct voice in product direction.
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Proximity to the User: A culture of speaking directly with customers to understand their friction points before writing a single line of code.
