Associate Data Engineer

at  Egen Solutions

Egen is a data engineering and cloud modernization firm helping industry-leading companies achieve digital breakthroughs and deliver for the future, today. We are catalysts for change who create digital breakthroughs at warp speed. Our team of cloud and data engineering experts are trusted by top clients in pursuit of the extraordinary. An Inc. 5000 Fastest Growing Company 7 times, and recently recognized on the Crain’s Chicago Business Fast 50 list, Egen has also been recognized as a great place to work 3 times.
As an Associate Data Engineer, you will be responsible for investigating data integration issues, analyzing the scope of the issue, communicating status and impact, and owning both immediate resolution and long-term prevention of the recurrence of data issues to business processes within cloud-native data environments like GCP, AWS, and Snowflake. As you advance you will be expected to look for opportunities for efficiency and data pipeline optimization and resiliency through the automation of the ingestion process.
The ideal candidate is very resourceful and has a strong foundation in Python logic and coding fundamentals, complex SQL procedures, ETL and Data warehousing processes, git deployments, relational databases, data mapping, data transformations, and data integration.

REQUIRED EXPERIENCE:

• Bachelor’s degree in a relevant field
• Relevant work experience in technology consulting and/or in a system or data analyst role
• Must have strong professional knowledge of and experience with SQL Server, Powershell, and SSIS
• Must have solid understanding of ETL and Data Warehousing processes

• Understand the flow, dependencies, provenance and service levels of our data systems to continuously improve accuracy, timeliness, and availability of data
• Manage the handoff of data ingestion streams from implementation teams to full production
• Take ownership and resolve all data issues
• Manage data providers to maintain escalation protocol, communicate inbound scorecards, and track resolution of issues
• Update mappings, transformations and validations to correct issues and keep pace with changes in inbound data
• Create and document processes to reduce operating cost and improve quality
• Conduct quality assessments and validation of end result data transfer
• Identify additional areas for integration of technology infrastructure and other IT systems
• Automate…automate…automate.