Lead Data Engineer (Snowflake)

Caterpillar Inc.•Irving, TX

2d•$128,470 - $192,710•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.