Data Engineer - Technical/User support

Caterpillar Inc.•Irving, TX

2d•$89,210 - $133,810•Onsite

About The Position

This role is responsible for designing, building, and operating scalable data solutions that enable reliable analytics and insight across the business. The role focuses on developing robust data pipelines, integrating data from multiple sources, and creating data models that support reporting and advanced analysis. Working closely with data scientists, analysts, and business stakeholders, the role ensures data is accurate, well‑governed, and performant, enabling teams to make confident, data‑driven decisions at scale.

Requirements

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, disciplines, 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 (Physical): 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.
Strong hands‑on expertise in SQL and Python, using them to build, optimize, and support reliable data pipelines and analytical solutions.
Proven experience working with modern data platforms and ETL tools such as Snowflake or equivalent technologies to deliver scalable, production‑ready data solutions.
Deep understanding of relational and cloud‑native databases, including PostgreSQL, MySQL, Snowflake, and BigQuery, with the ability to design efficient data models for analytics.
Practical experience operating in AWS environments, leveraging services such as Glue, Lambda, and Step Functions to orchestrate and automate data workflows.
Strong problem‑solving and analytical capability, able to diagnose complex data issues and design clear, effective solutions.
Solid understanding of distributed systems and data architecture, enabling the design of resilient, high‑performance data platforms.
A clear appreciation of data privacy, security, and governance best practices, ensuring data solutions meet compliance and enterprise standards.

Responsibilities

Build and manage ETL (Extract, Transform, Load) pipelines to move data from source systems (like databases, APIs, or logs) to data warehouses or lakes.
Design scalable data models and storage solutions that support analytics and reporting needs.
Integrate data from various internal and external sources, ensuring consistency and reliability.
Tune queries and systems for performance, especially when dealing with large datasets.
Implement checks to ensure data accuracy, completeness, and compliance with data governance policies.
Work closely with data scientists, analysts, and business stakeholders to understand data needs and deliver solutions.
Support with debug and analyze data issues raised from user queries and provide timely Root Cause Analyses (RCAs) within defined SLAs.
Collaborate with the Data Engineering team to assess, design, and implement new scope or evolving business requirements.
Communicate issue status, impact, and resolution clearly to stakeholders and end users.