Data Engineer

At Levi Strauss & Co, we are revolutionizing the apparel business and redefining the way denim is made.

We are taking one of the world’s most iconic brands into the next century: from creating machine learning-powered denim finishes to using block-chain for our factory workers’ wellbeing, to building algorithms to better meet the needs of our consumers and optimize our supply chain.

Be a pioneer in the fashion industry by joining our Digital Technology organization where you will have the chance to build exciting solutions that will impact our Americas business and at the same time be part of a bigger, across-continents, data community.

The Data, Analytics & AI/ML Engineering team at Levi is on a mission to build and deliver a modern data platform that will accelerate converting data into insights.  As the Senior Data Engineer within this org, you will work in of the Operations data domain teams responsible for bringing data from from various source systems into Google Cloud to the point of cleansing and delivering data in the form of data products to our end users. We need someone who will bring thoughtful solutions, perspective, empathy, creativity, and a positive attitude to solve tough data problems.

You are a proactive and self-driven engineer looking to take on challenges and the opportunity to grow. Taking ownership and driving results while being a team player are all critical ingredients. You are logical and have strong reasoning to balance solution vs risk taking.

Job Description

• Design, develop, and maintain scalable and reliable big data solutions, including data pipelines, data warehouses, and data lakes.
• Collaborate with cross-functional teams including data product, data scientists, analysts, and software engineers to understand data requirements and deliver robust solutions.
• Architect and optimize data storage, processing, and retrieval mechanisms for large-scale datasets.
• Establish scalable, efficient, automated processes for data analyses, model development, validation, and implementation.
• Implement and maintain data governance and security best practices to ensure data integrity and compliance with regulatory standards.
• Write efficient and well-organized software to ship products in an iterative, continual-release environment.
• Reporting key insight trends, using statistical rigor to simplify and inform the larger team of noteworthy story lines that impact the business.
• Troubleshoot and resolve performance issues, bottlenecks, and data quality issues in the big data infrastructure.
• Provide technical guidance and mentorship to junior engineers, fostering a culture of continuous learning and growth.
• Communicate clearly and effectively to technical and non-technical audiences.
• Contribute to and re-use community best practices.
• Embody the values and passions that characterize Levi Strauss & Co., with empathy to engage with colleagues from multiple backgrounds.

About You

• University or advanced degree in engineering, computer science, mathematics, or a related field
• 5+ years' experience developing and deploying data pipelines both batch and streaming into production.
• Strong experience working with a variety of relational SQL and NoSQL databases.
• Extensive experience working with cloud native data services in one of the popular public clouds (preferably GCP)
• Deep expertise in one of the popular data warehousing tools such as Snowflake, Big Query, RedShift, etc
• Experience working with big data tools and frameworks such as Hadoop, Spark, DBT, Kafka, etc.
• Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.[SB1] [VN2] 
• Hands-on experience in the Data engineering Spectrum, e.g. developing metadata-based framework-based solutions for Ingestion, Processing, etc., building Data Lake/Lake House solutions.
• Strong knowledge of data pipeline and workflow management tools (Ex: Airflow).
• Working knowledge of Github /Git Toolkit.
• Experience with providing operational support to stakeholders.
• Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation.
• Experience with data visualization using Tableau, PowerBI, Looker or similar tools is a plus
• Hands-on experience in the Data engineering Spectrum, e.g. developing metadata-based framework-based solutions for Ingestion, Processing, etc., building Data Lake/Lake House solutions. [SB1]
• Strong knowledge of data pipeline and workflow management tools (Airflow).
• Working knowledge of Github /Git Toolkit.