Principal Data Engineer

October 26

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IDEXX

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Diagnostics • Software • Veterinary

10,000+

💰 Seed Round on 1984-01

Description

• Work with ML research scientists to enable product-related ML modeling. • Design and implement end-to-end data workflows for ML model building and monitoring. • Impact search, retrieval, processing, tagging, and publishing of curated data sets. • Design and implement scalable, reliable distributed data processing frameworks. • Monitor structural performance and utilization, identify problems, and implement solutions. • Lead the creation of standards, best practices, and new processes for operational integration. • Ensure environments are compliant with defined standards and operational procedures. • Implement measures to ensure data accuracy and accessibility. • Collaborate with data scientists and analysts to understand their data needs. • Develop and maintain data systems, processes, and procedures documentation. • Complete problem tickets, including bug fixes, design modifications, and enhancements.

Requirements

• 5 or more years of experience working in machine learning and have delivered solutions into production in a professional setting. • 5 or more years of experience using Python, SQL, Spark • Experience with Databricks, Spark, with Big Data. • Your technical background is in Artificial Intelligence (AI) and Machine Learning (ML). • Experience owning a technology product and assuming a technical lead role. • Proficient in coding and programming languages such as Structured Query Language (SQL) and Python. • Familiarity with R will be an advantage. • Familiar with cloud platforms such as Amazon Web Services (AWS). • Experience or a good understanding of: - Hadoop-based technologies like MapReduce and Spark - SQL-based technologies like Oracle, PostgreSQL and MySQL - Data processing tools including DLT - Cloud-based data platforms, including Databricks and Snowflake - data warehousing solutions and relational database theory - industry-standard software APIs. • Good verbal and written communication skills and can translate technical subject matter to non-technical audiences. • Take the initiative in resolving problems and can balance conflicting requirements in partnership with others. • Excel at customer service and building relationships. • Experience building distributed and cloud-based data pipelines.