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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer (Snowflake) - **Company:** Caterpillar - **Location:** East Peoria, IL, United States - **Experience:** Expert - **Salary:** $128,470.0 - $192,710.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Architectural Patterns, ARM Architecture, Microsoft Azure, Cloud Computing, Information Systems, Databases, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Mart, Data Structures, Data Systems, Database Design, DevOps, Information Management, Interoperability, Python (Programming Language), Knowledge Management, Machine Learning, Metadata, Metadata Standards, Performance Tuning, Query Optimization, Standard Sql, Scala (Programming Language), Search Technologies, Software Engineering, Enterprise Data Management, System Availability, Snowflake, Generative AI, Infrastructure as Code (IaC), Data Layers, Build Management, Infrastructure Automation Frameworks, Information Technology, Data Lineage, Performance Monitor, Data Management, Virtual Agents, Data Pipelines, Programming Languages - **Published:** July 15, 2026 - **Apply:** https://dejobs.org/x/x/D215D4E173AA49ACBE633F40C92D301C/job/ ## About the Role * Bachelor's degree in computer science, Information Systems, Data Engineering, Software Engineering, or related technical field (or equivalent experience) * 10+ years of experience in data engineering or related disciplines with increasing responsibility * Expert knowledge of Snowflake architecture, security, performance tuning, and workload management. * Strong SQL, data modeling, and data architecture skills. * Experience building enterprise-scale ELT/ETL pipelines and data integration solutions. * Experience with cloud platforms (AWS, Azure, or GCP). * Proficiency in Python, Java, Scala, or similar modern programming languages. Top Candidates Will Have: * SnowPro Core or Advanced Snowflake certifications. * Experience designing enterprise-scale Snowflake architectures. * Experience supporting machine learning, generative AI, or agentic AI solutions through enterprise data platforms. * Familiarity with Snowflake Cortex AI, semantic models, vector search, and AI-ready data architecture. * Experience working with structured and unstructured data sources at scale. * Experience designing data products and supporting analytics consumption patterns. * Experience implementing data governance, metadata, lineage, and quality frameworks. * Knowledge of CI/CD, DevOps, Infrastructure as Code (IaC), and platform automation practices. * Strong communication skills with the ability to translate technical concepts into business value. Skills Descriptors: * Value Realization: Knowledge of value realization methods; ability to plan, execute, monitor and manage business activities and resources to determine and achieve the actual value from a business initiative as estimated in an associated business case. * Communicating Complex Concepts: Knowledge of effective presentation tools and techniques to ensure clear understanding; ability to use summarization and simplification techniques to explain complex technical concepts in simple, clear language appropriate to the audience. * Agile Development: Knowledge of agile methodologies and the agile development lifecycle; ability to utilize formal agile methodologies, disciples, practices and techniques for the delivery of new and enhanced applications. * Cloud Computing: Knowledge of cloud-based solutions and their applications; ability to design, implement, and manage cloud-based solutions to meet business needs and improve operational efficiency. * Database Design: Knowledge of database systems; ability to establish a data model for designing an organization's database that runs effectively and efficiently for better business outcome. * ETL Process: Knowledge of the extraction, transformation and loading (ETL) process; ability to develop a database through the ETL process. * Information Management: Knowledge of an organization's existing and planned information Architecture and Information Management (IM) methodology; ability to collect and manage information from different sources and distribute this information to enhance operational efficiency. * Modeling: Data, Process, Events, Objects: Knowledge of data, process and events; ability to use tools and techniques for analyzing and documenting logical relationships among data, processes or events. ## Description We are seeking a highly skilled Lead Data Engineer to design, build, and scale modern data solutions within a cloud-based environment, with a strong emphasis on Snowflake. This role combines hands-on engineering excellence with technical leadership, guiding a small team of data engineers while delivering high-quality, reliable, scalable, and AI-ready data products. This individual will play a critical role in enabling data-driven decision-making, advanced analytics, machine learning, and generative AI initiatives by building trusted data products, scalable pipelines, reusable semantic models, and governed datasets that support business and technology outcomes. What You Will Do: Data Engineering & Architecture: * Design and build scalable data ingestion pipelines from structured and unstructured data sources into Snowflake. * Develop and maintain ELT/ETL processes to transform, cleanse, and integrate enterprise data. * Design reusable dimensional, semantic, and business-ready data models that support analytics and AI use cases. * Build and maintain consumable data products including curated datasets, data marts, semantic layers, APIs, and AI-ready data assets. * Design and implement enterprise data architectures that support scalability, interoperability, and future AI adoption. AI-Ready Data Platforms: * Design and curate AI-ready datasets that support machine learning, generative AI, intelligent agents, and advanced analytics. * Implement metadata, lineage, and semantic modeling capabilities that improve data discoverability and AI readiness. * Collaborate with AI and analytics teams to establish patterns for retrieval, search, knowledge management, and AI-enabled business solutions. * Evaluate and adopt emerging Snowflake capabilities, including Cortex AI, semantic models, vectorized data structures, and AI-related platform services. Data Products & Governance: * Apply Data Product Management principles by establishing ownership, quality standards, service levels, and lifecycle management processes. * Implement data quality monitoring, automated validation, observability, and governance frameworks. * Ensure compliance with enterprise security, privacy, regulatory, and data governance requirements. * Establish and maintain metadata standards, data lineage, business definitions, and cataloging practices. Performance, Reliability & FinOps: * Optimize Snowflake performance through query tuning, workload management, storage optimization, and architectural improvements. * Ensure high availability, reliability, and scalability across data platforms and pipelines. Implementing proactive monitoring, alerting, and observability practices. * Drive cloud and Snowflake cost optimization through consumption monitoring, capacity planning, and engineering best practices. Leadership & Delivery * Lead and mentor a team of 3-4 data engineers, providing technical leadership, coaching, and career development. * Serve as the technical subject matter expert for Snowflake, modern data platforms, and AI-ready data engineering practices. * Define and enforce enterprise data engineering standards, architectural patterns, and development best practices. * Collaborate with business stakeholders, product owners, analysts, architects, and data scientists to translate business objectives into scalable data solutions. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Got AI ideas but no money? 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