IT Data Engineer (Snowflake)

The IT Data Engineer will play a key role in Infor’s Snowflake migration and modernization initiative, designing and developing scalable Snowflake-based data solutions that support analytics, automation, and AI-driven capabilities. The role is primarily focused on hands-on Snowflake development, including data ingestion, data pipeline development, semantic layer creation, and Snowflake Cortex-enabled AI initiatives. The ideal candidate brings strong expertise in Snowflake, SQL, Python, and modern data warehousing concepts, coupled with strong communication skills, accountability, and a growth mindset. Beyond technical execution, this individual will contribute to the team's Snowflake adoption journey through collaboration, knowledge sharing, and continuous improvement while helping drive the success of high-visibility modernization projects. A Typical Day in the Life Includes: Data Platform & Engineering Design, build, and optimize scalable, secure, and high-performing data solutions in Snowflake including ingestion, transformation, modeling, and consumption layers. Develop and maintain robust ELT/ETL pipelines, data workflows, and transformation frameworks to support reporting, analytics, operational use cases, and AI initiatives. Build and optimize Snowflake data models using best practices for performance, maintainability, scalability, and cost efficiency. Ensure strong data quality, reliability, observability, security, and governance through testing, monitoring, documentation, and operational best practices. Drive continuous improvement in data engineering practices including CI/CD, code reviews, reusable frameworks, automation, and production support readiness. Semantic Layer & AI Readiness Lead the implementation of semantic layer foundations using tools such as Snowflake Cortex Analyst YAML, dbt Metrics, or similar semantic modeling frameworks that make business data easier to discover, understand, and consume. Help prepare the data platform for AI readiness, including enabling structured, governed, and well-documented data assets that support AI/ML, copilots, intelligent agents, and natural language data experiences. Support data contracts and data product design practices that ensure upstream/downstream data reliability and enable scalable AI-ready data sharing across teams. Leverage Snowflake-native capabilities including Horizon Catalog, Cortex-powered workflows, streams, tasks, and dynamic tables for metadata visibility, cataloging, lineage, and observability. Contribute to enablement of modern tooling such as dbt, MCP-style integration patterns, Kiro, and Copilot/AI-assisted engineering practices. Stakeholder Engagement & Delivery Act as an embedded engineering partner—engaging directly with projects end to end, from requirements clarification, data assessment, and design through build, deployment, testing, and post-production support. Partner closely with business stakeholders, analysts, data scientists, architects, and cross-functional engineering teams to shape and deliver solutions. Translate technical concepts clearly for both technical and non-technical audiences; confidently present designs, recommendations, trade-offs, and progress updates to senior leadership. Mentor associate engineers, promote engineering standards, and contribute to a culture of continuous learning, collaboration, and delivery excellence. Basic Qualifications: Experience in data engineering, analytics engineering, or a related technical role, with strong exposure to modern cloud data platforms. Solid hands-on expertise in Snowflake as a core data platform, including experience with data ingestion and loading patterns, ELT design and implementation, performance tuning and query optimization, virtual warehouse sizing and workload management, cost optimization, secure data sharing and access control, as well as Snowflake-native capabilities such as Streams, Tasks, Dynamic Tables, and other platform features. Solid proficiency in SQL and Python for data transformation, automation, and engineering workflows. Knowledge of Data Vault methodology, including hubs, links, and satellites, and its application within modern data warehousing environments. Solid experience designing and implementing scalable data models, including dimensional modeling, Data Vault modeling, business-friendly consumption models, and semantic-ready structures. Experience building and maintaining production-grade data pipelines with a strong focus on reliability, observability, performance, and maintainability. Strong understanding of modern data warehousing architecture, metadata-driven development, and best practices for building analytics-ready data solutions. Experience supporting AI-readiness initiatives, including preparing trusted, governed, and well-structured data for downstream AI, analytics, and automation use cases. Strong stakeholder engagement skills, with the ability to gather requirements, shape solutions, and collaborate effectively across technical and business teams. Excellent communication, facilitation, and presentation skills, with the ability to explain complex technical concepts clearly and influence decision-making. Strong ownership mindset with the ability to independently drive workstreams and deliver outcomes across the full project lifecycle. Contribution-motivated team player who actively looks for opportunities to improve platforms, optimize processes, and create business value. Preferred Qualifications: Hands-on experience with dbt (data build tool) for transformation, testing, documentation, and modular analytics engineering practices. Experience with orchestration tools such as Airflow or similar workflow schedulers. Familiarity with Snowflake AI and governance capabilities, such as Snowflake Cortex / Cortex-powered development or code assistance, Snowflake Horizon Catalog, Semantic layer or metadata-driven data enablement, and Governance and discoverability capabilities for AI-ready data Exposure to MCP-style integration patterns, modern data product design, or emerging AI/agent-enablement frameworks. Experience with data contracts and data product design to support reliable, governed data sharing across teams and AI consumers. Experience using Copilot, Kiro, or similar AI-assisted engineering tools to improve productivity, code quality, documentation, and delivery speed. Experience with CI/CD pipelines, Git-based development workflows, and release management for data engineering assets. Strong knowledge of data governance, lineage, cataloging, security, and compliance best practices in enterprise environments. Experience working directly with business programs or product teams in a forward-deployed engineering or embedded delivery model. Experience contributing to architecture discussions, technical roadmaps, and platform modernization initiatives.