Data Engineer
4 months ago
  • Domain IT Software
  • Availability Full-time
  • Experience Senior Level
  • Type of contract Indeterminate term
  • Location Bucuresti
  • Salary To be determined

Requirements

• Mastery in SQL

• Mastery of Scala and/ or Python

• Knowledge of Elastic Search

• Knowledge of Hadoop, Kafka, Nifi, Flume, scikit-learn, Jupyter/ Zeppelin, R

• Knowledge of Spotfire, ggplots, matplotlib, bokeh

• Knowledge of Tensorflow, Keras, Theano

• Expertise in the implementation of end-to-end data processing chains

• Mastery of distributed development

• Basic knowledge and interest in the development of ML algorithms

• Knowledge of the ingestion framework

• Knowledge of Spark and its different modules

• Knowledge of the AWS or GCP ecosystem (Cloud or Google Cloud Platform)

• Knowledge of the ecosystem of NOSQL databases

• Knowledge in the construction of APIs of data products

• Knowledge of Dataviz tools and libraries

• Ability to debug Spark and distributed systems

• Popularization of complex systems

• Knowledge of algorithm complexity

• Mastering the use of notebooks data

• Expertise in data testing strategies

• Strong problem solving, intelligence, initiative and ability to withstand pressure

• Excellent interpersonal skills and a great sense of communication (ability to go into detail)

• Fluent in English (verbal and written)


Responsibilities

During project definition:

• Design of data ingestion chains

• Design of data preparation chains

• Basic ML algorithm design

• Data product design

• Design of NOSQL data models

• Design of data visualizations (Spotfire, Qlikview, Power BI)

• Participation in the selection of services / solutions to be used according to the uses

• Participation in the development of a data toolbox


During the iterative realization phase:

• Implementation of data ingestion chains

• Implementation of data preparation chains

• Implementation of basic ML algorithms

• Implementation of data visualizations

• Using ML framework

• Implementation of data products

• Exposure of data products

• Setting up NOSQL databases (Cassandra, Elasticsearch, MongoDB)

• Implementation in distributed mode of treatments

• Use of functional languages

• Debugging distributed processes and algorithms

• Identification and cataloging of reusable elements

• Contribution to the evolution of labor standards

• Contribution and opinion on data processing problems


During integration and deployment:

• Participation in problem solving

VALUES
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