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Data Engineer

Salary undisclosed

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  • Build and maintain optimal data platform and pipeline architecture.
  • 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.
  • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and Google Cloud Platform big data technologies.
  • Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
  • Create data tools for analytics team members that assist them in building and optimizing our product into an innovative industry leader.
  • Work with data and analytics experts to strive for greater functionality in our data systems.


What you'll bring:

  • Advanced working knowledge with SQL, No-SQL databases.
  • Experience building and optimizing big data data pipelines, architectures and data sets.
  • Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
  • Strong analytic skills related to working with unstructured datasets.
  • Build processes supporting data transformation, data structures, metadata, dependency and workload management.
  • A successful history of manipulating, processing and extracting value from large, disconnected datasets.
  • Working knowledge of message queuing, stream processing, and highly scalable big data data stores.
  • Strong project management and organizational skills.
  • Experience supporting and working with cross-functional teams in a dynamic environment.
  • We are looking for a candidate with 3 to 6 Years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools:
  • Experience with big data tools: Hadoop, Spark, Kafka, etc.
  • Experience with relational SQL and NoSQL databases, including Big Query, Cassandra.
  • Experience with data pipeline and workflow management tools: Airflow, etc.
  • Experience with Google Cloud Platform cloud services: GCS, Dataproc, Dataplex, etc.
  • Experience with stream-processing systems: Spark-Streaming, Storm etc.
  • Experience with object-oriented/object function scripting languages: Python, Scala.
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