Job Description Working with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs Keeping our data separated and secure across national boundaries through multiple data centers and AWS\GCP regions Assembling large, complex data sets that meet functional/non-functional business requirements Creating and maintaining optimal data pipeline architecture Creating data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader 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 Databricks, 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 data and analytics experts to strive for greater functionality in our data systems Requirements 5 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 SQL knowledge and experience working with relational and NoSQL databases, query authoring (SQL) as well as working familiarity with a variety of databases Experience in designing, building and optimizing data pipelines, and data sets Experience in performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement Experience in setting up and implementing data platforms such as new infrastructure, VPC setting, security settings and configurations on cloud platform such as Azure, GCP or AWS 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 in supporting and working with cross-functional teams in a dynamic environment They should also have proven experience using the following software/tools: E Experience with relational SQL and NoSQL databases, including Postgres, Oracle, mySQL and mongoDB Experience with Databricks (mandatory) Experience with data pipeline orchestration and workflow management tools: Airflow, Astronomer, etc.
Experience with AWS cloud services: S3, EC2, EMR, RDS, Redshift, and Glue Experience with GCP cloud services: GCS, Dataproc, GCE, BigQuery, and GKE Experience with object-oriented/object function scripting languages: Python (mandatory), Java, C++, Scala, etc.
Experience with Git Requirements SQL knowledge and experience working with relational and NoSQL databases, query authoring (SQL) as well as working familiarity with a variety of databases Experience in designing, building and optimizing data pipelines, and data sets Experience in performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement Experience in setting up and implementing data platforms such as new infrastructure, VPC setting, security settings and configurations on cloud platform such as Azure, GCP or AWS 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 in supporting and working with cross-functional teams in a dynamic environment They should also have proven experience using the following software/tools: E Experience with relational SQL and NoSQL databases, including Postgres, Oracle, mySQL and mongoDB Experience with Databricks (mandatory) Experience with data pipeline orchestration and workflow management tools: Airflow, Astronomer, etc.
Experience with AWS cloud services: S3, EC2, EMR, RDS, Redshift, and Glue Experience with GCP cloud services: GCS, Dataproc, GCE, BigQuery, and GKE Experience with object-oriented/object function scripting languages: Python (mandatory), Java, C++, Scala, etc.
Experience with Git
