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FH
Fischer Homes
SENIOR DATA ENGINEER
Career Insights for Generative Artificial Intelligence Engineer
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What they do
A Generative Artificial Intelligence Engineer develops, designs, and manages generative models and algorithms that support the generation of new content in the form of images, text, audio, and other multimedia. They utilize GPTs, GANs, VAEs, and other deep learning architectures to craft systems capable of generating data. May work with data scientists, machine learning engineers, and software developers.
$116,064 / year median in Kentucky
Job Description
Job Summary As a Senior Data Engineer, you will play a key role in building and evolving the data foundation that powers decision-making across The Fischer Group. You'll design, develop, and maintain scalable data pipelines and modern Lakehouse/Warehouse solutions on the Microsoft Fabric platform, ensuring business data is accurate, reliable, and analytics-ready. In this role, you'll help modernize our data ecosystem by migrating legacy solutions, establishing engineering best practices, and partnering with Analytics, Business Intelligence, and IT teams to deliver trusted data that drives business insights. If you enjoy solving complex data challenges, building cloud-native solutions, and shaping the future of enterprise analytics, this role offers the opportunity to make a lasting impact. This position is 100% onsite, day 1 in our Erlanger, KY office. You will thrive in this role if you: Enjoy designing scalable data solutions that enable better business decisions. Are passionate about building reliable, high-performing cloud data platforms. Take ownership of data quality, governance, and pipeline reliability. Enjoy solving complex technical challenges while continuously improving existing processes. Thrive in collaborative environments where you partner with Analytics, Business Intelligence, and Software Development teams. Stay current with emerging technologies and enjoy applying modern engineering practices to improve efficiency. Like mentoring others and helping establish engineering standards across a growing team. These skills will be used to: Design, build, and maintain scalable ETL/ELT pipelines using Microsoft Fabric and related cloud technologies. Develop and optimize Lakehouse and Data Warehouse architectures for performance, scalability, and cost efficiency. Modernize legacy SSIS and on-premises data solutions by migrating them to cloud-based platforms. Build reliable, automated data ingestion processes using incremental loading, orchestration, and monitoring best practices. Implement data quality testing, validation, and governance to ensure trusted, analytics-ready datasets. Partner with Analytics Engineering to deliver clean, curated data that supports enterprise reporting and self-service analytics. Establish reusable engineering patterns, documentation, and development standards for the Data Engineering team. Troubleshoot and resolve production pipeline issues while continuously improving reliability and operational performance. Mentor junior engineers and contribute to continuous improvements in source control, CI/CD, and deployment processes.