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Developing scripts for the extraction of structured and non-structured information (e.g. SQL databases, csv, xlsx, xml, txt, web pages and others).
Design data models considering best practices regarding efficiency and information security.
Create dedicated scripts to perform data transformation and store in redundant repositories.
Evaluate and collaborate in the implementation of solutions for handling big data environments.
Optimize legacy ETL processes and perform new developments.
Document the data processing pipelines and manage the code versioning.
Collaborate with the engineering team in the development or modification of the core existing platforms.
Contribute to publications
Take operational responsibility for the components that you develop.

Deep understanding of the web scrapping approach, web pages structures and techniques to extract information.
Using of parallelism in the execution of ETL process, execution based on events.
Using of Amazon cloud computing resources like EMR and S3 storage.
Use of Graph Database Neo4j and Cypher query language.
Working in an environment where you constantly experiment and iterate quickly

Experience using Python as programing language focus on data field (e.g. Anaconda, Jupyter Notebook, Pandas, Dask, PySpark, Numpy, Scikit, others).
Experience with the RDBMS and of SQL query language, database optimizations, indexing, replications.
Experience with NOSQL resources, like ElasticSearch, MongoDB, Apache Cassandra,etc.
Data lake principles and practical experience with big data solutions (e.g. Hadoop, Apache Spark)
Experience with deployment ETL process in Docker containers.
Experience with secure data at transit and rest (encryption) and techniques for clustering and replication implementation.