Sr. Data Engineer with Machine Learning

Responsibilities:
Design, program, and create ML algorithms to address business challenges and enhance data processing tasks.
Develop, build, and deploy ML models into production environments.
Utilize PySpark, Databricks/Spark, and experience with any ML framework to drive efficient data engineering solutions.
Collaborate with cross-functional teams to identify data requirements and provide data-driven insights.
Qualifications:
ML - Data Processing on Machine Learning Concepts: Strong understanding and practical experience in data processing within the context of machine learning concepts.
Databricks: Proficiency in utilizing Databricks, with the ability to answer questions and demonstrate expertise in its usage.
Cloud Technology - Azure or GCP: Hands-on experience with cloud technologies such as Azure or Google Cloud Platform (GCP). Share your knowledge and explain your preferred choice of technology.
Spark - Intermediate: Intermediate-level proficiency in Apache Spark, particularly in Spark architecture and PySpark utilization.
Pyspark - Intermediate: Intermediate-level experience in utilizing PySpark for data engineering and analysis tasks.
Databases - NoSQL and SQL: Experience with both NoSQL and SQL databases. Be prepared to discuss the types of databases you have worked with and answer related questions.
In addition to the above, experience with Databricks is a must-have for this role. We also value cloud experience with Google Cloud Platform (GCP) or Azure, familiarity with Snowflake, and expertise in working with NoSQL databases.