Big Data Engineers
K-LAGAN
Madrid, ES
hace 32 días

At K-LAGAN we are looking for Big Data Engineers to join our team in one of the most important Telecommunications Companies in Spain, located in Madrid & Valladolid .

You will be working on the collecting, storing, processing, and analysing of huge sets of data. The primary focus will be on choosing optimal solutions to use for these purposes, then maintaining, implementing, and monitoring them.

You will also be responsible for integrating them with the architecture used across the company.

Stack : Scala, Spark, Hadoop stack, SQL, MongoDB, Jenkins, Github, AWS, PostGIS, Unix, Bash.

Responsibilities

  • Selecting and integrating any Big Data tools and frameworks required to provide requested capabilities.
  • Monitoring performance and advising any necessary infrastructure changes.
  • Assemble large, complex data sets that meet functional / non-functional business requirements.
  • Identify, design, and implement internal process improvements : automating manual processes, optimizing data delivery, re-
  • designing infrastructure for greater scalability, etc.

  • Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
  • Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
  • Requirements

  • 2 years’ experience in Scala and Spark.
  • 3 years’ experience in development with languages, such as Java.
  • Experience with integration of data from multiple data sources.
  • Knowledge of NoSQL databases, such as HBase, Cassandra, MongoDB.
  • Experience with Big Data ML toolkits, such as Mahout, SparkML, or H2O.
  • Experience with best coding practices like continuous integration, code reviews, continuous deployment and related tools.
  • Fluent Spanish and English.
  • Nice to have

  • Good Knowledge about tools and good practices for migration large volumes of data.
  • AWS, Lambda, Redshift, Athena.
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