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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior / Mid-Level Data Engineer - **Company:** TEAM Inc. - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $120,000.0 - **Contract:** Internship / Graduate position - **Skills:** Sql Data Warehouse, Application Programming Interfaces (APIs), Artificial Intelligence, ARM Architecture, Big Data, Cloud Engineering, Continuous Integration, Data as a Services, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Structures, Data Warehousing, Cursor, Database Queries, Software Debugging, Document-Oriented Databases, JSON, Python (Programming Language), Machine Learning, Operational Databases, Standard Sql, Azure Machine Learning, Search Technologies, Software Engineering, SQL Databases, Software Technical Review, Feature Engineering, GitHub Copilot, Large Language Models, Claude Code, Snowflake, AI Coding Agents, Git, Event Driven Architecture, Containerization, Semi-structured Data, Real Time Data, Data Management, Video Streaming, Code Restructuring, Software Version Control, Data Pipelines - **Published:** October 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8717c3407724ccd7 ## About the Role The ideal candidate has a minimum 3-5 years of hands-on data engineering experience, strong SQL and Python skills, and working experience with modern cloud data platforms. Just as importantly, this person already builds with AI, using AI coding assistants in day-to-day development and understanding how LLMs and ML services fit into a modern data platform. You do not need to be an ML engineer. You do need AI tooling to be part of how you already work, and you need the judgment to know when its output is wrong., * Minimum 3-5 years of experience in Data Engineering, Software Engineering, or a related field (internships and co-ops count toward this). * Strong SQL skills and hands-on experience with a modern cloud data platform, preferably Snowflake. * Solid proficiency with Python and Sound Software Development Practices. * Experience building and maintaining ELT/ETL pipelines. * Working understanding of data modeling and data warehousing concepts. * Experience integrating structured and semi-structured data from multiple sources. * Familiarity with data orchestration and transformation frameworks. * Working knowledge of version control (Git) and branch-based collaboration. * Strong troubleshooting instincts and a desire to become a world-class data and SQL debugger. * Clear written and verbal communication, and the ability to work independently in a remote-first environment. AI Qualifications * These qualifications are the floor, not the ceiling. We expect every engineer on the team to meet this bar on day one! * Daily working use of at least one AI coding assistant (Claude Code, GitHub Copilot, Cursor, or similar) for writing, refactoring, debugging code and the judgment to recognize when its output is wrong. * Practical prompt-writing ability: can break a real task into clear, scoped instructions and iterate toward a correct result rather than accepting the first answer. * Conceptual understanding of how LLMs work at a high level; tokens, context windows, what causes hallucination, and why model output must be validated before it touches production data. * Familiarity with calling an LLM via API (any provider) from Python, including handling structured/JSON output. * Basic understanding of embeddings and vector search, and what problems they are and are not suited for. * Awareness of AI data-governance basics: what client, consumer, or PII data may not be sent to a third-party model, and why. * Willingness to bring AI-assisted approaches to the team and share what works., * Automotive industry data experience, including vehicle, dealership, CRM, inventory, consumer, telematics, finance, aftermarket, or mobility datasets. * Experience with Matillion preferred, dbt welcome. * Experience with Snowflake Cortex, or with LLM and ML features native to a cloud data warehouse. * Experience building a retrieval-augmented generation (RAG) pipeline, even at a personal-project level. * Familiarity with machine learning data pipelines and feature engineering concepts. * Experience with streaming technologies and event-driven architectures. * Experience supporting Business Intelligence, Data Science, and AI initiatives. * Experience with CI/CD, infrastructure-as-code, or containerization. * Experience working in a remote-first environment. ## Description * Build and maintain scalable data pipelines and ELT/ETL workflows. * Develop and optimize data models and data structures that support reporting, analytics, machine learning, and operational applications. * Use Snowflake and modern cloud data platforms to ingest, transform, and optimize large datasets. * Partner with Business Intelligence and Data Science teams to deliver trusted, production-ready datasets. * Build and maintain the data foundations that AI and ML workloads depend on; clean, well-documented, well-governed datasets and feature tables. * Implement data quality, validation, and monitoring processes. * Support both batch and near real-time data integration requirements. * Contribute to APIs and data services that enable internal and customer-facing applications. * Use AI coding assistants to accelerate development, while owning and validating everything that ships under your name. * Improve data pipeline performance, scalability, and cost efficiency. * Document data architectures, pipeline processes, and engineering standards. * Participate in code and design reviews and help uphold engineering best practices.