Role Purpose:

At the Analysis, Build, Testing and Implementation stages of the data or analytics project a data engineer is involved. The data engineer is responsible for the technical analysis, design, implementation, maintenance and technical support of the data or analytics project or change.

Experience and Qualifications:

  • Prefer relevant IT, Engineering, Applied Mathematics or Statistics Degree or Diploma
  • At least 5 years experience as a Data Engineer
  • Solid knowledge of SQL, databases, data warehousing, ETL and other data tools
  • Solid experience in Python and other scripting languages
  • Experience in either AWS (preferred), Azure, Hadoop and/or Spark.
  • Prefer some experience with Analytics/Bigdata/ML tools and modelling

Responsibilities and Work Outputs:

  • Create and maintain optimal data pipeline, data platform and tooling
  • Automating manual processes and optimizing data delivery
  • Consult with and guide business, users, BAs, System Analysts, developers, architects, other data engineers and other roles players in terms of data and analytics
  • Analyse requirement specifications to be able to realize the project or change within the data architecture and BUs architecture as a whole
  • Before building, create or update technical design documentation, information architecture and discuss with technical role-players
  • Implementations complying to the security standards and practices
  • Breaking up the project or changes into parts and tasks so that development can be done in a managed and systematic fashion
  • Create and maintain code, scripts, queries, transformations, models or programs, manage tools, databases, warehouses, and analytical systems of high-quality that fulfils the requirement specifications
  • Assist in maintaining the information architecture and Meta-data
  • Create and run tests as specified by the test cases, scenarios and validate data
  • Where applicable do technical reviews on the more Juniors tasks
  • Do source control, build, deploy and implement code, scripts, queries, transformations or models within the established DevSecOps and change management processes and practices
  • Investigate, debug, trace and resolve issues in non-prod and production environments
  • Monitor, tracing and support production
  • Keep abreast with data and analytics tools, programming and scripting languages, frameworks, trends, tech, etc. and keep skills up-to-date and relevant

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