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Senior Data Engineer
4 weeks ago
What you will be doing Day to Day:
- Collaborate closely with the data product team and business end-users to create and maintain data platforms with best-in-class technology and methodology.
- Create robust and scalable data ingestion and data management solutions for batch and streaming data sources.
- Collaborate with the data governance team to develop the data quality check methodology and ensure that data issues are monitored, tracked, and resolved without violating SLAs.
- Collaborate with team members to develop best practices and internal processes to improve data pipeline operations.
Requirements:- Bachelor’s degree in Computer Science, Computer Engineering or equivalent
- At least 5 years’ experience of working as a data engineer or backend developer in a big data field
- Solid working knowledge of implementing the optimal data structures and algorithms to create efficient and scalable applications in Java or Python
- Familiar with application integration with RDBMS.
- Hands-on experience of using Linux (or Unix-like OS) as the development environment and familiar with shell scripts and command line tools in Linux/Unix environment
- Exposure and knowledge in the following technologies is advantageous:
- Java, Python, Shell Script, Informatica, Node.js
- Snowflake, Oracle, MS-SQL
- IBM-MQ, Kafka, Amazon Kinesis, Apache Flink
- AWS
- Docker, Kubernetes
- Data Virtualization – Denodo
- Experienced with the Systems Development Life Cycle implementation methodology (SDLC) and/or agile methodologies like Scrum and Kanban
- Understand and apply the good industry practice of code versioning, testing, CICD workflow and code documentation
- Data Virtualization – Denodo
- Docker, Kubernetes
- AWS
- IBM-MQ, Kafka, Amazon Kinesis, Apache Flink
- Snowflake, Oracle, MS-SQL
- Java, Python, Shell Script, Informatica, Node.js
- Collaborate with team members to develop best practices and internal processes to improve data pipeline operations.
- Collaborate with the data governance team to develop the data quality check methodology and ensure that data issues are monitored, tracked, and resolved without violating SLAs.
- Create robust and scalable data ingestion and data management solutions for batch and streaming data sources.
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