The posting
The Company We are representing a well-capitalized, fast-growing B2B technology company that builds high-volume data aggregation platforms. Their infrastructure ingests massive streams of sensor and mobility data from hundreds of fragmented third-party sources, unifying it into a single, reliable API. Some of the largest enterprises in the logistics, transportation, and risk-management sectors rely on this backbone for real-time analytics. The engineering team operates out of a Toronto hub, tackling petabyte-scale challenges with the agility of an early-stage startup and the rigor of a mature tech organization. The Role This is a foundational backend and systems engineering position, not a traditional data engineering or pipeline-maintenance role. You will be architecting the core distributed systems that ingest, process, and store terabytes of time-series data daily. You will own the full lifecycle of the platform’s data flow, building the highly available, resilient primitives that internal product teams and external enterprise clients build upon. Core Responsibilities Design, build, and optimize high-throughput data processing systems (both real-time streaming and batch). Engineer robust storage solutions capable of handling rapidly expanding volumes of time-series and spatial data without compromising on query performance. Drive architectural decisions to ensure the infrastructure remains fault-tolerant and ahead of the company's aggressive scaling trajectory. Develop intelligent, automated tooling that improves data quality, lineage tracking, and system reliability. Collaborate closely with internal stakeholders to define technical abstractions that can be reused across different product lines. Write scalable, production-ready code, primarily utilizing Java and Python. Requirements Candidate Profile 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. Technical Foundation: Deep expertise in computer science fundamentals, system architecture, and anticipating failure modes in complex networks. Language Proficiency: Advanced proficiency in a JVM language (Java, Scala, Kotlin) and a willingness to work across different stacks as needed. Data Ecosystems: Hands-on experience with modern large-scale processing frameworks (e.g., event streaming, distributed computation, and advanced data warehousing/lakehouse concepts). 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 Active contributions to open-source software, particularly in the distributed systems or data infrastructure space. Familiarity with modern orchestration engines, lakehouse architectures, or spatio-temporal data models. Benefits Work Environment High Autonomy: Engineers own their domains end-to-end and have a direct voice in product direction. Proximity to the User: A culture of speaking directly with customers to understand their friction points before writing a single line of code.
