Data Engineer IV

Grainger is hiring a Data Engineer IV for its Central Data Platform team, which builds governed, reliable data products across Databricks and Snowflake. The role designs and operates data pipelines, platform patterns, semantic models, and AI-enablement capabilities, while partnering with business and technical teams and mentoring colleagues.

Responsibilities:

• As the senior technical engineer, design and implement highly efficient, reusable, and scalable data processing systems and pipelines across the tech stack, including Kubernetes, Databricks, Snowflake, and related cloud services
• Help define platform patterns for AI-ready data products, including governed data assets, semantic models, vector and search capabilities, retrieval patterns, and integration points for Snowflake, Databricks, and other AI-enabled workflows
• Support Snowflake AI initiatives by evaluating, implementing, and operationalizing capabilities such as Cortex Analyst, Cortex Search, Cortex Agents, Cortex Code, Cortex Sense, and related AI/LLM-enabled data engineering features
• Support Databricks requests from data engineering and analytics teams, including job orchestration, reusable pipeline patterns, performance tuning, production troubleshooting, workspace standards, and best-practice guidance
• Use and evaluate AI-assisted engineering tools such as Claude, Claude CLI, GitHub Copilot, and related agentic tooling to improve delivery while maintaining secure, reviewed, and reliable engineering practices
• Design with test-driven development and implement technical solutions to ensure data reliability, accuracy, observability, and operational resilience
• Develop data models and mappings, build new data assets required by users, and perform exploratory data analysis on existing products and datasets
• Educate data engineering teams in adopting new data patterns, platform capabilities, AI-enablement approaches, and tools
• Understand trends and emerging technologies, including the shift toward multi-agent engineering environments, and evaluate the performance and applicability of potential tools for Grainger requirements
• Work within an Agile delivery / Kanban methodology to deliver product increments in iterative cycles
• Work with product and business partners to define roadmap, communication, architecture, adoption plans, and support models
• Mentor junior team members and help raise the technical bar for data engineering practices across the organization

Requirements:

• Position is not eligible for visa sponsorship
• 3+ years of experience in batch and streaming data engineering using Spark, Python, Scala, Snowflake, or Databricks for analytics, data engineering, or machine learning workloads
• 3+ years orchestrating and implementing production pipelines with workflow tools such as Databricks Workflows / Lakeflow Jobs, Apache Airflow, Luigi, or similar orchestration platforms
• Hands-on Databricks platform experience in a central data platform or data engineering enablement role, including notebooks/repos, Delta Lake, Unity Catalog, Databricks Runtime, job/serverless/cluster compute, workflow monitoring, cost and performance optimization, and production support of user requests
• Experience or strong working familiarity with Snowflake AI and Cortex capabilities such as Cortex Analyst, Cortex Search, Cortex Agents, Cortex Code / Snowflake CoCo, Cortex Sense, AISQL / AI functions, semantic models, and AI-enabled search, retrieval, or agent patterns
• Comfort working with AI-assisted developer and agent tools such as Claude, Claude CLI, GitHub Copilot, or similar tools; understanding of emerging multi-agent system patterns, including context management, tool use, orchestration, guardrails, testing, observability, and secure enterprise adoption
• 5+ years of experience preparing structured and unstructured data for data science, analytics, machine learning, or AI-enabled applications
• 4+ years of experience with containerization and orchestration technologies such as Docker and Kubernetes
• Experience working with a variety of databases, including but not limited to vector databases, graph databases, relational databases, and NoSQL stores
• Experience using machine learning or AI in data pipelines to discover, classify, enrich, standardize, and clean data
• Experience implementing CI/CD with automated testing in Jenkins, GitHub Actions, GitLab CI/CD, or similar tooling
• Familiarity with AWS or other cloud services such as AWS Glue, Athena, Lambda, S3, and DynamoDB
• Demonstrated experience implementing the data management lifecycle using data quality functions such as standardization, transformation, rationalization, linking, matching, monitoring, and stewardship
• experience with shell scripting in Bash, Unix, or Windows shell