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Mid-level Data Engineer

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Mid-level Data Engineer

Phoenix, Arizona, United States

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Job Openings Mid-level Data Engineer

About the job Mid-level Data Engineer

Minimum Qualifications

  • Masters in computer applications or equivalent OR bachelors degree in engineering or computer science or equivalent.
  • Deep understanding of Hadoop and Spark Architecture and its working principle.
  • Deep understanding of Data warehousing concepts.
  • Ability to design and develop optimized Data pipelines for batch and real time data processing.
  • Experience in data analytics and cleansing
  • 5+ years of software development experience.
  • 5+ years experience on Python or JavaHands-on experience on writing and understanding complex SQL (Hive/PySpark-data frames), optimizing joins while processing huge amount of data.
  • 3+ years of hands-on experience of working with Map-Reduce, Hive, Spark (core, SQLand PySpark).
  • Hands on Experience on Google Cloud Platform (BigQuery, DataProc, Cloud Composer)
  • Hands on Experience in Airflow
  • 3+ years of experience in UNIX shell scripting
  • Should have experience in analysis, design, development, testing, and implementation of system applications.
  • Ability to effectively communicate with internal and external business partners.

Additional requirements

  • Understanding of Distributed eco system.
  • Experience in machine learning models, RAG, and NLP
  • Experience in designing and building solutions using Kafka streams or queues.
  • Experience with NoSQL i.e., HBase, Cassandra, Couchbase or MongoDB
  • Experience with Data Visualization tools like PowerBI, Tableau, SiSense, Looker
  • Ability to learn and apply new programming concepts.
  • Knowledge of Financial reporting ecosystem will be a plus.
  • Experience in leading teams of engineers and scrum teams

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Skills

  • Phoenix
  • Hadoop
  • Spark
  • Python
  • SQL
  • Hive
  • PySpark
  • GCP
  • BigQuery
  • Dataproc
  • Cloud Composer
  • Airflow
  • Unix
  • Shell
  • Machine Learning
  • Retrieval-Augmented Generation
  • NLP
  • Kafka Streams
  • HBase
  • Cassandra
  • Couchbase
  • MongoDB
  • Power BI
  • Tableau
  • Looker

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