Grainger Management LLC is looking for a Senior Data Engineer to support its Product Discovery organization. In this hybrid role based in Lake Forest, IL, you will design and operate governed batch and real-time data products that help power search, machine learning, analytics, and AI-enabled experiences.
The work centers on building reliable pipelines, orchestration patterns, and search or retrieval capabilities using modern data and cloud-native technologies. You will partner with technical and business stakeholders to translate needs into scalable data solutions and operationally owned capabilities.
Location: Lake Forest, IL (Hybrid)
Salary range: USD 104,500 - 174,200 per year
Responsibilities
- Design and operate reliable, governed batch and real-time data products using technologies such as Databricks, Snowflake, Spark, Kubernetes, vector/search technologies, and cloud-native services.
- Build pipelines, data assets, orchestration patterns, and search and retrieval capabilities to provide trusted data for search, machine learning, analytics, and engineering teams.
- Design, develop, and maintain scalable data solutions that integrate and transform data for business and analytical use.
- Apply data engineering practices to deliver reliable, high-quality data products while improving performance, scalability, and automation of data processes.
- Translate requirements from technical and business stakeholders into effective data solutions.
- Design, develop, test, and maintain scalable data pipelines and data products integrating information from multiple sources.
- Extract, transform, integrate, and deliver reliable data for analytical and business needs.
- Develop and enhance data engineering capabilities to improve data accessibility, performance, quality, and scalability.
- Analyze requirements and translate business and technical needs into data designs, specifications, and solutions.
- Identify and implement improvements to data engineering processes, including automation, optimization, and increased reliability.
- Partner with stakeholders to resolve data-related challenges and deliver solutions aligned with business requirements.
- Provide technical guidance, review designs, and help establish engineering practices for less-experienced team members.
- Design and deliver scalable batch and streaming data pipelines and data products across modern data and cloud platforms.
- Define reusable data engineering patterns for CDC, event-driven processing, workflow orchestration, data products, and AI/search-enabled workloads.
- Partner with Engineering, Product, and Applied Science to shape architecture, roadmap, adoption, and operational ownership of data capabilities.
Requirements
- Bachelor's Degree or equivalent experience in Computer Science, Computer Engineering, Information Systems, Data Science, Software Engineering, or a closely related technical field.
- 3+ years of related data engineering or software engineering experience.
- Experience building and operating production batch and streaming data pipelines on Databricks or similar modern data platforms.
- Strong software engineering skills in Python with hands-on experience using Spark or other distributed processing frameworks.
- Hands-on experience with CDC, streaming/event-driven architectures, and workflow orchestration, including selecting appropriate patterns based on scale, reliability, and use case.
- Experience with containerization and orchestration such as Docker and Kubernetes, plus cloud platforms such as AWS or equivalent.
- Experience implementing CI/CD and automated testing using platforms such as GitHub Actions or equivalent.
- Experience working across relational, NoSQL, vector, graph, or other database technologies and selecting appropriate storage patterns based on use case.
- Experience designing and developing scalable data pipelines, data integration solutions, and data products.
- Proficiency with programming, scripting, and data query languages used to develop and manipulate data solutions.
- Experience translating requirements into technical designs and implementing solutions involving multiple data sources.
- Knowledge of modern data engineering.
Technologies: Databricks, Snowflake, Spark, Kubernetes, vector/search technologies, Python, Docker, AWS, GitHub Actions, CDC, streaming/event-driven architectures, workflow orchestration, CI/CD, NoSQL, graph, relational, vector, Tableflow, Airflow, Databricks Workflows, AWS DMS, Debezium, machine learning, AI
Benefits
- Medical, dental, vision, and life insurance plans with coverage starting on day one of employment.
- 6 free sessions each year with a licensed therapist to support emotional wellbeing.
- 18 paid time off (PTO) days annually for full-time employees (accrual prorated based on employment start date).
- 6 company holidays per year.
- 6% company contribution to a 401(k) Retirement Savings Plan each pay period, with 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.
Compensation
- Anticipated base pay: $104,500.00 – $174,200.00 per year.
- Eligible for an incentive target of up to 10% or $, based on individual and company performance objectives under the current incentive program terms (subject to change).
Additional Information
- This position is not eligible for sponsorship now or in the future. Individuals requiring sponsorship (for example, OPT or H1B visa status) should not apply.
- Only individuals authorized to work in the United States now and for the foreseeable future will be considered.
Preferred
- Experience with CDC and orchestration platforms such as AWS DMS, Debezium, Tableflow, Airflow, Databricks Workflows, or equivalent.
- Experience using machine learning or AI within data pipelines to classify, enrich, standardize, match, or improve data.
- Experience with e-commerce, search, recommendations, or personalization platforms.
- Working knowledge of machine learning models and production of ML workflows.
