Futran Tech Solutions Pvt. Ltd.
Senior Specialist - Software Engineer
Futran Tech Solutions Pvt. Ltd., Jersey City, New Jersey, United States, 07390
JOB DETAIL
Ability to develop, modify and adopt tools and processes to support self-service data pipeline managementDrive adoption of the these data tools to modernize existing ETL frameworks/processesCollaborate with business and technology partners across the organization to assess data needs and prioritize adoption accordinglyIdentify additional strategic opportunities to evolve the data engineering practiceRequired Skills:
10+ years of experience in data engineeringExperience building production data pipelines using frameworks using PYTHON, Spark, Hive/Hadoop .Hands on experience with schema design and data modeling10+ years of software development in a major language .3 plus years of experience to common Python packages, such as PySparkMinimum 3 to 5 years of practical working experience in Relational Databases, preferably Oracle or Oracle ExadataHave a software engineering mindset and strive to write maintainable, and testable codeStrong SQL skills and knowledge of relational databases (Oracle etc) and familiarity with other data stores such as Hadoop, data storesExperience with performance tuning data transformations across large data setsExceptional problem solving skills & Excellent communication skillsUnderstanding of the Agile methodologyDesired Skills:
BS/MS in Computer Science, Engineering or other quantitative disciplineKnowledge of financial conceptsKnowledge of cloud or distributed computingExperience with AirflowExperience with BI Platforms such as MicroStrategy or TableauSoftware development in an Agile environmentWorking experience with Git, Jira, ConfluenceExcellent written and oral communication skillsPassion for automation and continual process improvementAbility to develop, modify and adopt tools and processes to support self-service data pipeline managementDrive adoption of the these data tools to modernize existing ETL frameworks/processesCollaborate with business and technology partners across the organization to assess data needs and prioritize adoption accordinglyIdentify additional strategic opportunities to evolve the data engineering practice
Ability to develop, modify and adopt tools and processes to support self-service data pipeline managementDrive adoption of the these data tools to modernize existing ETL frameworks/processesCollaborate with business and technology partners across the organization to assess data needs and prioritize adoption accordinglyIdentify additional strategic opportunities to evolve the data engineering practiceRequired Skills:
10+ years of experience in data engineeringExperience building production data pipelines using frameworks using PYTHON, Spark, Hive/Hadoop .Hands on experience with schema design and data modeling10+ years of software development in a major language .3 plus years of experience to common Python packages, such as PySparkMinimum 3 to 5 years of practical working experience in Relational Databases, preferably Oracle or Oracle ExadataHave a software engineering mindset and strive to write maintainable, and testable codeStrong SQL skills and knowledge of relational databases (Oracle etc) and familiarity with other data stores such as Hadoop, data storesExperience with performance tuning data transformations across large data setsExceptional problem solving skills & Excellent communication skillsUnderstanding of the Agile methodologyDesired Skills:
BS/MS in Computer Science, Engineering or other quantitative disciplineKnowledge of financial conceptsKnowledge of cloud or distributed computingExperience with AirflowExperience with BI Platforms such as MicroStrategy or TableauSoftware development in an Agile environmentWorking experience with Git, Jira, ConfluenceExcellent written and oral communication skillsPassion for automation and continual process improvementAbility to develop, modify and adopt tools and processes to support self-service data pipeline managementDrive adoption of the these data tools to modernize existing ETL frameworks/processesCollaborate with business and technology partners across the organization to assess data needs and prioritize adoption accordinglyIdentify additional strategic opportunities to evolve the data engineering practice