Data Engineer
Information Technology
portugual Lisboa Jun 24, 2020
Data Engineer
  • Requirements:

- Bachelor's degree in IT and/or comparable job expertise (min. 3 years)

- Programming with functional programing and object-oriented script languages: Scala, Python, Java

- Tooling Experience

· Experience with big data tools and distributed clusters: Hadoop, Spark, Kafka, Kinesis, etc.

· Experience with relational SQL and NoSQL databases, including Postgres and Cass andra.

· Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, Glue Workflow etc.

· Experience with AWS cloud services: Athena, EC2, Elastic, Elasticache (Redis),

EMR, Glue, RDS, Redshift


  • Technical Qualifications:

-Experience with stream-processing systems: Flink, Storm, Spark-Streaming etc.

-Advanced working SQL knowledge and experience working with relational databases,  query authoring (SQL) as well as working familiarity with a variety of databases.

-Experience buil ding and optimizing big data’ data pipelines, architectures and data sets.

- Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.

-Buil d processes supporting data transformation, data structures, metadata, dependency and workload management.

-A successful history of manipulating, processing and extracting value from large distributed datasets.

- Strong analytic skills related to working with unstructured datasets.

-Build- and Source cont rol -tools like, Gitlab, GradIe, Maven, Artifactory, Bit buc ket

- Knowledge of buil ding pipelines and automation especially with AWS infra structure-a s- a -code mechanism (Cloudformation, Sceptre, Serverless)

- Understand principles around security, logging and monitoring running products Experience in Test- Driven- Development (TDD), Behavior- Driven- Development (BDD) Experience in Pair programming, Scrum, Kanban, Design thinking.

  • Softs Skils

- Willingness to work in a diverse, m ulticultural, interdisciplinary balanced product team

- Continuous learning and improvement Sharing knowledge and d ocument each step

- Motivated to work in an agile setup Bringing in own innovative ideas

- Think outside the box and to hel p overcome roadblocks

- Result-oriented and communicative team player International working experience

- Good English skills (written and spoken
 


  • Responsabilities:

-Create and maintain data pipeline architecture.

- Assemble large, complex data sets that meet functional / non -functional business requirements.

- I dentify, desig n, and implement internal process improvements: automating man ual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.

- Buil d the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ’big data’ technologies.

- Buil d 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 inc luding the Exec utive, Product, Data and Design teams to assist with data-related technical issues and support their data infra structure needs.

-Keep our data separated and sec ure across national boundaries through m ultiple data centers and AWS regions.

- Create data tools for analytics and data scientists that a ssist them in building and optimizing our product into an innovative industry leader.

-Work with data and analytics experts to strive for greater functionality in our data system s.

- Actively contribute to tech and architecture decisions Write clean code following the TDD/BDD approach

- Implementation and maintenance of CI/CD pipelines

-Understand and implement principles around sec urity, log ging and monitoring running products

-Use build-measure-learn feedback loops to constantly improve all aspects of our work

-Experience in Operational Readiness (Functionality, Reliability, Usability, Efficiency, Chanpeability, Transferability)