Explore and assemble large, complex data sets that meet functional / non-functional business requirements.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Spark, EMR, Kafka and other big data technologies
Work with stakeholders across different teams, including product managers, engineers and analysts to assist with data-related technical issues and support their data infrastructure needs.
Education & Requirements
Bachelor’s degree in Computer Science, a related technical field, or equivalent professional experience.
Minimum of 5 years of professional software engineering experience, with a strong focus on data and ML processes running in production at scale.
Experience with optimizing big data and ML pipelines like Spark and Kafka at scale
Fundamental knowledge of ML and AI operations and processes. Experience in MLFlow, Feast, Ray, and other AI training technologies a plus
Understanding of a variety of database technologies such as OpenSearch, Druid, Clickhouse, InfluxDB, etc
Proven track record of cross-functional data modeling
Strong command of SQL and expertise in one or familiarity with multiple of the following languages: Python, Scala, Java
Experience in containerization, K8s and ArgoCD is a plus
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