Implement data pipelines to automate the ingestion, transformation, and augmentation of data sources, and provide best practices for pipeline operations.
Troubleshoot and remediate data quality issues raised by pipeline alerts or downstream consumers.
Work with upstream QA teams on integrating data validation tests. Evaluate new releases from data quality perspective.
Provide advice and ideas for technical solutions and improvements to data systems.
Create and maintain clear documentation on data models/schemas as well as transformation/validation rules.
Requirements :
3-6 years' experience in Data Engineer role that involves dimension modeling and SQL-based (expert level) data transformation and analytics.
Exposure to cloud (AWS) big data technologies
Hands-on experience with all aspects of designing, developing, testing and implementing ETL solutions (Pentaho).
Fluent in programming languages like Java/Python.
Comfortable working in Linux environment.
Experience building complex reporting dashboards.
Past experience working with large scale columnar data stores (Redshift/Vertica).
Good to have:
Experience with Metabase.
Take responsibility for performance tuning.
Ability to develop and organize high-quality documentation.
Superior analytical skills and a strong sense of ownership in your work.
Exposure to cloud big data technologies.
Ability to thrive in a fast-paced start-up environment, and to manage multiple, competing priorities simultaneously.
Knowledge in orchestration tools like Airflow
Keyskills: hive vertica scala amazon redshift big data technologies pyspark data pipeline sql java data science spark linux pentaho mysql hadoop big data python performance tuning natural language processing airflow machine learning nosql pandas tableau aws
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