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Hydrolix - Portland, OR
posted 4 days ago
Full-time - Mid Level
Portland, OR
Securities, Commodity Contracts, and Other Financial Investments and Related Activities
About the position
The Hydrolix data lake platform allows data scientists to drastically improve their model training and development velocity. Our platform integrates seamlessly with state-of-the-art model development technologies. At Hydrolix, you'll play a pivotal role in building infrastructure solutions targeted at the developer ecosystem for model development. You'll find creative ways to unlock the power of the Hydrolix platform for a broad set of developers.
Responsibilities
- Develop robust, efficient and performant backend systems for querying and processing massive datasets.
- Design, build, and optimize integrations with distributed processing frameworks like Apache Spark.
- Collaborate with various teams including, Core Platform, Data Intake, API, Product and Sales to understand customer needs and develop innovative solutions.
- Contribute to research and engineering decisions, particularly in the areas of integration with Spark environments in AWS EMR, Microsoft Fabric and Snowflake.
- Enhance our data processing capabilities and develop advanced features.
- Work across various components of our platform, from data ingestion pipelines to performance optimization and third-party integrations.
Requirements
- Highly experienced backend distributed systems developer, with a proven ability to deliver high quality production software on Linux/Unix platforms.
- Proficiency in one or more compiled programming languages Scala or Java.
- Experience with distributed systems and distributed processing frameworks, such as Spark, Hive, Trino.
- Familiarity with open table formats like Apache Iceberg, Apache Hudi and Delta Lake.
- Familiarity with open storage formats like Parquet and Avro.
- Curious mindset with a demonstrated ability to learn new technologies and programming languages.
- Exceptional analytical, communication, and problem-solving skills.
