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Your expertise in ETL, DataBricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupiter Notebooks will be essential in ensuring efficient data processing and analysis. - High level of proficiency in ETL processes and demonstrated hands-on experience with technologies such as DataBricks, Python, Spark, Scala, JavaScript/ JSON, SQL, and Jupyter Notebooks.
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Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake. 77 West Wacker Dr (35012), United States of America, Chicago, IllinoisSenior Manager, Data Engineering (Spark, Scala, SQL.
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What Additional Experience Makes A Strong Candidate: 4+ years of production experience with Scala 4+ years of production experience with Micro Services / Streaming Services Experience addressing ML problems, particularly in domains such as Time Series, Computer Vision (CV), Natural Language Processing (NLP), and data mining Proficiency with popular ML frameworks (Xgboost, TensorFlow, PyTorch, etc.
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You have at least one year of experience in any JVM based languages (such as Java, Kotlin, Scala) and are confident in your ability to build, debug and ship microservices. The ideal candidates have strong knowledge of distributed systems design, are proficient in coding on the JVM (one of Java, Kotlin, Scala), understand the importance of scalability and reliability and are looking to be part of cutting-edge projects in a fast-paced environment.
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3+ years of experience in batch and streaming ETL using Spark, Python, Scala, Snowflake, or Databricks for Data Engineering or Machine Learning workloads. 3+ years of experience in batch and streaming ETL using Spark, Python, Scala, Snowflake, or Databricks for Data Engineering or Machine Learning workloads.
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Perform complex application design and programming activities in the latest techniques: Scala, Java, Node, REST, microservices, AWS/Cloud infrastructure, Spark, Kubernetes, Docker, Jenkins. 8+ years of application development using Java, Python, and/or Scala at all levels of the application stack (front end/UI, middle/services and back end/database.
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7+ years of experience in application development including Scala, Databricks, Python, 7+ years of experience in application development including Scala, Databricks, Python, At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud.
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7+ years of experience in application development including Python, SQL, Scala, or Java. 4+ years of data warehousing experience (Redshift or Snowflake) 4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud.
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9+ years of experience in application development including Python, SQL, Scala, or Java. 9+ years of experience in application development including Python, SQL, Scala, or Java. Lead a team of developers with deep experience in machine learning, distributed microservices, and full stack systems.
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Tech stack includes Airflow, Spark, Databricks, Delta Lake, Snowflake, Scala, Python. Architect, design, and code shared libraries in Scala and Python that abstract complex business logic to allow consistent functionality across the Data organization.
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4+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) Work with a team of developers with deep experience in machine learning, distributed microservices, and full stack systems.
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Strong fundamental Scala and Python programming skills. Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day – from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.
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Experience with at least one major Massively Parallel Processing (MPP) or cloud database technology (Snowflake, Redshift, Big Query) Hands-on production experience with orchestration systems such as Airflow.
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This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Experience with Container Orchestration services including Docker and Kubernetes.
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Expanding, scaling, and standardizing the core foundational principles through consistent observability, lineage, data quality, logging, and alerting across all engineering teams in the Data organization is imperative to the creation of a single pane of glass.
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