Quick overview
Job description
Responsibilities:
- A Data Engineer should be able to build, operationalize 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)
- 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:
- Strong professional technical experience
- Hands on DBT experience.
- Hands-on Data Warehousing.
- Experience building highly scalable data solutions using Hadoop, Spark, Databricks, Snowflake
- Experience in programming languages such as Python
- Experience working in cloud environments (Azure)
- Experience in Redshift
- Strong client-facing communication and facilitation skills
Key Skills:
- Python, Azure Cloud, Redshift, NoSQL, Git, ETL/ELT, Spark, Hadoop, Data Warehouse, Data Lake, Data Engineering, Snowflake, SQL/RDBMS, OLAP
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