Data Engineer

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Responsibilities

  • Create and maintain optimal data 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, etc.
  • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies.
  • Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
  • Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
  • Keep our data separated and secure across national boundaries through multiple data centers and AWS regions.
  • Create data tools for analytics and data scientist 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.

Qualifications

  • Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
  • Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of 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.
  • Strong project management and organizational skills.
  • Experience supporting and working with cross-functional teams in a dynamic environment.
  • Experience with different types of data dimensions and data warehousing techniques.

Technology

  • Experience with big data tools: Hadoop, Spark, Kafka, etc.
  • Experience with relational SQL and NoSQL databases, including Postgres, MSSQL,  and Cassandra.
  • Experience with data pipeline and workflow management tools: Oozie, Airflow, AWS Glue, AWS Step Function etc.
  • Experience with AWS cloud services: EC2, EMR, RDS, Redshift, etc.
  • Experience with AWS IAM Security & encryption tools : Vault, Protegrity, Voltage, etc.
  • Experience with stream-processing systems: Storm, Kinesis, Spark-Streaming, etc.
  • Experience with configuration as code systems: Terraform, Ansible, Puppet, Chef, Cloudform, etc.
  • Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.

 

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