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

Requirements

8+ 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

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data processing systemsdata pipelinesstructured dataunstructured dataSQLNoSQLELT processesdata lakedata architecturedata modeling

Soft Skills

client-facing communicationfacilitation skillstroubleshootingproblem-solvingteam collaborationcustomer supportroot cause analysis