Responsibilities:
- A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems
- Create robust and automated pipelines to ingest and process structured and unstructured data from various source systems into analytical platforms using batch and streaming mechanisms leveraging cloud native toolset
- Implement custom applications using tools such as Kinesis, Lambda and other cloud native tools as required to address streaming use cases
- Engineers and supports data structures including but not limited to SQL and NoSQL databases
- Engineers and maintain ELT processes for loading data lake (Snowflake, Cloud Storage, Hadoop)
- Engineers API’s for returning data from these structures to the Enterprise Applications
- Leverages the right tools for the right job to deliver testable, maintainable, and modern data solutions
- Respond to customer/team inquiries and assist in troubleshooting and resolving challenges
- Works with other scrum team members to estimate and deliver work inside of a sprint
- Research data questions, identifies root causes, and interacts closely with business users and technical resources
Qualifications:
- 6+ years of professional technical experience
- 4+ years of hands-on Data Architecture and Data Modelling.
- 4+ years of experience building highly scalable data solutions using Hadoop, Spark, Databricks, Snowflake
- 4+ years of programming languages such as Python
- 2+ years of experience working in cloud environments (AWS and/or Azure)
- Strong client-facing communication and facilitation skills
Key Skills
- Python, Cloud, Linux, Windows, NoSQL, Git, ETL/ELT, Spark, Hadoop, Data Warehouse, Data Lake, Snowflake, SQL/RDBMS, OLAP, Data Engineering
0 applies
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