Grainger is looking to enhance its data infrastructure and backend systems to support analytics, machine learning models, and customer-facing applications. This involves building scalable data pipelines, optimizing data platforms, and ensuring seamless data integration with enterprise systems like SAP S/4HANA.
Requirements
- 7+ years of experience designing, building, and supporting large-scale, production-grade software or data engineering systems.
- Hands-on experience with cloud platforms like AWS, GCP, or Azure, especially for data storage, processing, and orchestration.
- Proficiency with tools such as Snowflake, Databricks, PostgreSQL, and event-streaming platforms like Kafka.
- Strong knowledge of containerization (Docker, Kubernetes/OpenShift) and DevOps principles.
- Experience building RESTful APIs, event-driven pipelines, and integrating third-party systems and services.
- Experience with SAP data extraction and data model, specifically with SAP S4 and Hana, integrating through SAP Datasphere or similar sources.
- Experience with infrastructure as code tools like Terraform or CloudFormation, and automation of CI/CD pipelines.
Responsibilities
- Design, build, and maintain scalable, cloud-native data pipelines and ETL workflows using tools such as Apache Spark, AWS Glue, and Snowflake.
- Solution, develop, and rigorously test data products that support analytics, operational reporting, and real-time decision-making.
- Develop and deploy high-quality backend applications using Python, SQL, and Scala or other languages commonly used in data engineering environments.
- Build and maintain data platforms and structures such as data lakes, data warehouses, and APIs to support both real-time and batch use cases.
- Build, optimize, and support CI/CD pipelines, infrastructure as code (IaC), and deployment automation using Docker, Kubernetes, GitHub Actions, or similar tools.
- Develop clean, maintainable, and well-documented code, following best practices in testing (TDD), version control, and observability.
- Ensure data quality and integrity through automated validation frameworks and modern monitoring practices.
Other
- Hybrid work location type.
- Partner with cross-functional teams (data scientists, software engineers, product, operations, and design) to deliver data-driven business solutions.
- Mentor junior engineers and interns, particularly in areas such as data platform architecture, data product development, and engineering best practices.
- Excellent communication skills, strong documentation practices, and a collaborative mindset with a passion for mentoring others.
