Required Skills

Data Engineer Hadoop Hive Spark ETL Big Data data processing

Work Authorization

  • Citizen

  • Work Permit

Preferred Employment

  • Full Time

Employment Type

  • Direct Hire

education qualification

  • UG :- B.Tech

  • PG :- Any PG

Other Information

  • No of position :- ( 1 )

  • Post :- 27th Nov 2019

JOB DETAIL

Data Engineer - Startup (1-5 yrs)

Position Summary

As a Data Engineer, a part of Machine Learning team, you will be responsible for creating data-driven products using scalable machine learning platforms/APIs and data processing technologies. Share your resume at [email protected]

Requirements and General Skills

  • Have all round experience in developing and delivering large-scale business applications in scale-up systems as well as scale-out distributed systems.
  • Responsible for design and development of application on different data platforms.
  • Should implement complex algorithms in a scalable fashion. Core data processing skills are highly important with tools like HIVE/Impala
  • Should be able to write MapReduce jobs or Spark jobs for implementation. Ability to write Java-based middle layer orchestration between various components on Hadoop/spark stack.
  • Work closely with product and Analytic managers, user interaction designers, and other software engineers to develop new offerings and improve existing ones.

Personal Skills

  • Good Analytical & Problem-Solving Skills
  • Good Communication Skills: Refers to effective oral, written and presentation skills
  • Responsible Team player with go-getter attitude

Technical Skills

    • B.Tech or Master’s degree from a reputed university in Computer Science or equivalent disciplines.
  • 1-3 years’ experience building software or web applications with object-oriented or functional programming languages. Doesn’t matter what language, just a focus on writing clean, well designed and scalable code on MapReduce.
  • Experience with Big Data technologies such as Hadoop, Hive, Spark, or Storm
  • Experience with streaming technologies like Kafka, Spark, Flink
  • Experience with scalable systems, large-scale data processing, and ETL pipelines
  • Experience with SQL and relational databases such as Postgres or MySQL
  • Experience with NoSQL databases like DynamoDB, CloudSearch, or open source variants like Cassandra, HBase, Solr, or ElasticSearch
  • Experience with DevOps tools (GitHub, Travis CI, JIRA) and methodologies (Lean, Agile, Scrum, Test Driven Development)
  • Experience building and deploying applications on on-premise and AWS cloud-based infrastructure

Company Information