Data Engineer (Remote) Job at Ecom Industries, United States

  • Ecom Industries
  • United States

Job Description

Data Engineer (Remote)

Basic Qualifications:

  • A minimum of three years developing and deploying large scale advanced analytics applications into a production environment
  • Minimum of five years, hands-on experience collaboratively developing code in object-oriented language for a production environment, including:
    • Experience working in Linux environment (bash scripting)
    • SQL (Data Normalization)
    • Python
    • Data Structures (arrays, lists, stacks, queues, trees, dictionaries, etc.)
    • Experience in data mining techniques and methodologies (data prep/modeling, classification, regression, clustering, causal modeling, AI, machine learning, ensemble approaches)
    • Advanced experience in data visualization tools with a strong grasp of effective data modeling and visualization practices
    • Experience rapidly developing proof of concepts and testing new ideas, as well as scale these ideas into production ready models
    • Experience leveraging cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure

Preferred Qualifications:

  • Advanced, relevant education, advanced Degree (Masters or PhD) in engineering, mathematics, physics, economics, computer science, statistics, or business analytics
  • Experience with AWS big data services (Kinesis, Glue, EMR, Spectrum etc.)
  • Experience with ETL & analytics tools (AWS, Matillion, Tallend, AtScale, etc.)
  • Experience with data virtualization technologies (Denodo, etc.)
  • Experience with data visualization tools (PowerBI, Tableau, etc.)
  • Experience with Scrum/Agile delivery processes and DevOps concepts (i.e. CI/CD)
  • Experience with data storage technologies (Redshift, S3, Hadoop, Graph, etc.)
  • Experience with cloud Platforms (AWS, Azure, Google)
  • Experience with multiple languages and syntax (Python, SQL, Apache Spark, etc.)
  • Experience with Container based orchestration of big data pipelines

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