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Lead Guest Experience Specialist - Denver (Seasonal Full Time, March to November 2027)
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Lead Guest Experience Specialist - Denver (Seasonal Part Time, March to November 2027)
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Lead Guest Experience Specialist - Glenwood Springs (Seasonal Full Time, March to November 2027)
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Lead Guest Experience Specialist - Glenwood Springs (Seasonal Part Time, March to November 2027)
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Lead Guest Experience Specialist - Salt Lake City (Seasonal Full Time, March to November 2027)
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Lead Guest Experience Specialist - Salt Lake City (Seasonal Part Time, March to November 2027)
Armstrong Collective Brand
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Lead Guest Experience Specialist - Moab (Seasonal Full Time, March to November 2027)
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Lead Guest Experience Specialist - Moab (Seasonal Part Time, March to November 2027)
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Driver’s License required

Grainger Businesses

Data Engineer IV

LAKE FOREST, IL, United States

The anticipated base pay compensation range for this position is $134,100.00 – $223,500.00.

Apply on jobs.grainger.com About employer

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Job details

Pay: The anticipated base pay compensation range for this position is $134,100.00 – $223,500.00.
Posting language: English
Posted: Nov 10, 2026

Original job description

Work Location Type: Remote

Req Number 336770

About Grainger

W.W. Grainger, Inc. is a leading broad line distributor with operations primarily in North America and Japan. At Grainger, We Keep the World Working® by serving more than 4.6 million customers worldwide with maintenance, repair and operating (MRO) products and value-added solutions delivered through innovative technology and deep customer expertise. Known for its commitment to service and purpose-driven culture, the Company reported 2025 revenue of $17.9 billion. For more information, visit www.grainger.com.

Compensation

The anticipated base pay compensation range for this position is $134,100.00 – $223,500.00. This role is eligible for an incentive target of up to 15 %, based on the achievement of individual and company performance objectives in accordance with the current terms of the incentive program which are subject to change.

Position is not eligible for visa sponsorship.

Rewards and Benefits

With benefits starting on day one, our programs provide choice and flexibility to meet team members' individual needs, including:

Medical, dental, vision, and life insurance plans with coverage starting on day one of employment and 6 free sessions each year with a licensed therapist to support your emotional wellbeing.
18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date) and 6 company holidays per year.
6% company contribution to a 401(k) Retirement Savings Plan each pay period, no employee contribution required.
Employee discounts, tuition reimbursement, student loan refinancing and free access to financial counseling, education, and tools.
Maternity support programs, nursing benefits, and up to 14 weeks paid leave for birth parents and up to 4 weeks paid leave for non-birth parents.

For additional information and details regarding Grainger’s benefits, please click on the link below:

https://experience100.ehr.com/grainger/Home/Tools-Resources/Key-Resources/New-Hire

Grainger Benefits

The pay range provided above is not a guarantee of compensation. The range reflects the potential base pay for this role at the time of this posting based on the job grade for this position. Individual base pay compensation will depend, in part, on factors such as geographic work location and relevant experience and skills.

The anticipated compensation range described above is subject to change and the compensation ultimately paid may be higher or lower than the range described above.

Grainger reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion at any time, consistent with applicable law. Position Details

Grainger's Central Data Platform team has evolved from a traditional data engineering function into a data engineering and AI enablement team. We build reliable, governed, real-time and batch data products across Databricks and Snowflake that power financial reporting, business analytics, data science, and emerging AI-enabled experiences.

In this role, you will design and operate the pipelines, data assets, semantic models, search and retrieval layers, and platform patterns that make trusted data available for analytics, AI agents, and operational decision-making. You will partner with domain experts and with AI, Platform, and Business Analytics teams to turn business problems into scalable data and AI-enablement solutions. You are a thoughtful technical leader who enjoys investigating business needs, building production-grade systems, and teaching other teams how to adopt the capabilities and products you create.

You Will

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.

You Have

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 with shell scripting in Bash, Unix, or Windows shell is preferred.
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.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, protected veteran status or any other protected characteristic under federal, state, or local law. We are proud to be an equal opportunity workplace.

We are committed to fostering an inclusive, accessible work environment that includes both providing reasonable accommodations to individuals with disabilities during the application and hiring process as well as throughout the course of one’s employment, should you need a reasonable accommodation during the application and selection process, including, but not limited to use of our website, any part of the application, interview or hiring process, please advise us so that we can provide appropriate assistance.

Apply on jobs.grainger.com About employer

More about this job

Skills

Data engineering
Databricks
Snowflake
Financial reporting
Decision making
Google Analytics
Process data
Kubernetes
Troubleshooting
GitHub
Organisational resilience
Data models
Data analysis
Communication skills
Apache Spark
Python
Scala
Machine learning
Workflow
Apache Airflow
Unity
Docker
Bash
UNIX
Microsoft Windows
NoSQL
CI/CD
Test automation
Jenkins
GitLab
AWS
Amazon DynamoDB
Data management

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Work requirements

Jobs in the United States