Senior Data Engineer

Fischer Homes•Erlanger, KY

48d•Onsite

About The Position

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.

Requirements

Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent professional experience.
Three (3)+ years of experience designing, building, and supporting enterprise data pipelines (ETL/ELT) in a production environment.
Strong proficiency in SQL and experience with Python (PySpark preferred).
Hands-on experience with Microsoft Fabric, Azure Data Factory, Azure Synapse, Databricks, Snowflake, or a similar cloud data platform.
Experience implementing data ingestion strategies, incremental loading, watermarking, and reliable pipeline architecture.
Experience with source control and CI/CD practices using Git or similar tools.
Strong analytical, problem-solving, communication, and organizational skills.

Nice To Haves

Microsoft Certified: Fabric Data Engineer Associate (DP-700), or actively pursuing certification.
Experience migrating legacy SSIS or on-premises data environments to cloud-based Lakehouse architectures.
Experience with data quality frameworks, pipeline monitoring, and data contracts.
Familiarity with dimensional modeling (Kimball methodology).
Experience working in Agile environments using Scrum or Kanban methodologies.
Experience mentoring or leading other Data Engineers.
Understanding of AI, Machine Learning, and generative AI technologies.
Experience implementing AI-enabled solutions or machine learning models within enterprise data environments.

Responsibilities

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.