• Competitive
  • Singapore
  • Permanent, Full time
  • Citibank NA
  • 2018-10-20

Big Data Engineering Lead Analyst

Big Data Engineering Lead Analyst

  • Primary Location: Singapore,Singapore,Singapore
  • Education: Bachelor's Degree
  • Job Function: Technology
  • Schedule: Full-time
  • Shift: Day Job
  • Employee Status: Regular
  • Travel Time: No
  • Job ID: 18062163


Description
  • The Big Data & Analytics Engineering Lead Analyst will contribute in application design and development aspects with focus on delivering end-to-end solutions. The candidate should have prior hands-on experience with Big Data technologies like kafka (preferable confluent kafka), spark streaming and working knowledge on Big Data tools.
Responsibilities:
  • Contribute to all Applied Engineering activities - requirements gathering, project planning, building, etc.
  • Drive the application design, development and delivery of any Big Data based requirements with application team.
  • Publish and enforce Hadoop best practices, configuration recommendations, usage design/patterns, and cookbooks to developer community.
  • Develop code for any customization/development required including visualizations.
  • Coordinate multiple offshore and onshore teams for development, setup and code rollout activities.
  • Kafka  SME and provide Level-3 technical support for troubleshooting


Qualifications
  • Hands-on knowledge of the Big Data technologies - Hadoop, Spark, Hive, Impala, HBase, Cloudera Manager, Sqoop, Flume, Pig, Kafka, Flume, Python, Shell Scripts, etc.
  • Experience with solution architecture, design, development and delivery of a full development life cycle. Experience with detailed level of data analysis using technical tools.
  • Should be able to develop code and self-contribute.
  • Hands-on working experience with one or more Hadoop distribution from Cloudera, Hortonworks, or MapR.
  • Experience with data wrangling tools and BI and reporting technologies - Paxata, Cognos, Datameer, Tableau, Arcadia, etc.
  • Understanding of data life cycle - data acquisition, data quality management, data governance, and metadata management.
  • Experience in large scale data warehouse implementations and knowledge of ETL technologies.
  • Excellent written and verbal communication skills.
  • Capable of building, articulating, and presenting new ideas to technical, non-technical, and business communities.
Education and experience:
  • Minimum bachelor degree required, master degree preferred
  • 6 to 10 years of experience in relevant technologies.