Senior Software Engineer – Spark
Speedata is modernizing analytics infrastructure with the first purpose-built ASIC processor, the Analytics Processing Unit (APU), for analytics and AI data workloads. Delivering up to 100x faster Apache Spark performance while cutting infrastructure TCO by 90%, the APU executes analytics operations directly in silicon with seamless integration and no code changes. We’re seeking an outstanding Senior SW Engineer to join our team and help us develop our data frameworks integrations. This is a rare opportunity to influence the future of data processing. Responsibilities: Develop our Spark plugin and integrations Develop in Scala and Java Contribute to open-source projects Develop in Python Design advanced algorithms of processing and optimizing query plans Define our query abstraction layer Collaborate with other software teams to optimize software and hardware integration for maximum performance Requirements : BSc or equivalent or higher degree in Computer Science, Computer Engineering, or Electrical Engineering. 8+ years of experience in software development with a strong focus on data engineering Strong proficiency in Java and Scala, Python - an advantage Experience with Apache Spark / other big data frameworks Excellent communication skills and ability to explain complex technical concepts to non-technical stakeholders Speedata is the creator of the Analytics Processing Unit (APU), the first processor purpose-built for Apache Spark SQL, batch ETL, and AI data preparation workloads. By executing Spark operations natively in silicon rather than memory, the APU delivers up to 100x faster performance and 90% lower total cost of ownership compared to general-purpose compute, with zero code changes required. In one enterprise AI data preprocessing deployment, customers replaced 37 servers with just 3. Speedata integrates directly into existing data pipelines, accelerating the most demanding analytics and AI data layer workloads at scale. Our hardware, the C200 APU card with the Callisto ASIC on PCIe Gen 5, delivers transparent acceleration for Spark SQL, DataFrame, and batch ETL workloads.
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