• Maintain and optimize cloud infrastructure to ensure reliability and scalability for data systems. • Extract, transform, and load (ETL) data across various systems to support business operations and decision-making. • Identify and resolve issues within existing systems to enhance performance, stability, and scalability. • Troubleshoot and fix bugs in deployed systems, ensuring minimal downtime and disruption. • Optimize systems and streamline existing processes to improve efficiency and reduce latency. • Build and optimize system deployment structures to ensure smooth, consistent deployments and system performance.

• Work collaboratively with Product Managers, Designers, and Engineers to set up, develop, and maintain critical back-end integrations for data and analytics platform. • Create and maintain new and existing data pipelines, Extract, Transform, and Load (ETL) processes, and ETL features using Azure cloud services. • Build, expand, and optimize data and data pipeline architectures. • Optimize data flow and collection for cross functional teams of database architects, data analysts, and data scientists. • Operate large-scale data processing pipelines and resolve business and technical issues pertaining to the processing and data quality. • Assemble large, complex sets of data that meet non-functional and functional business requirements. • Identify, design, and implement internal process improvements including re-designing data infrastructure for greater scalability, optimizing data delivery, and automating manual processes. • Develop and document standard operating procedures (SOPs) for new and existing data pipelines. • Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition. • Write unit and integration tests for all data processing code. • Read data specifications and translate them into code and design documents.

At Hugging Face, we’re on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 5 million users & 100k organizations who collectively shared over 1M models, 300k datasets & 300k apps. Our open-source libraries have more than 400k+ stars on Github. About the Role As our first Data/Infrastructure Advocate Engineer, you’ll bridge the gap between cutting-edge data infrastructure and the global community of data engineers, researchers, and developers. You’ll champion Xet storage on the Hugging Face Hub, empowering users to efficiently store, version, and collaborate on large-scale datasets. This role is for someone who thrives at the intersection of technical depth (storage, Parquet, deduplication) and community advocacy—helping define the future of open data workflows. You’ll collaborate with teams like Datasets, Hub, and Infrastructure to shape how developers interact with data on our platform, and inspire a community to build better, faster, and more scalable data pipelines. Your Main Missions: - Grow and nurture the open-source data/infra community—launch initiatives, collaborate with data-focused groups, and organize events or challenges. Engage with communities like Apache Parquet, Open Tables Formats, and data engineering forums to promote best practices and Hugging Face tools. - Promote the Hugging Face Hub as the go-to platform for data storage, versioning, and collaboration—curate and showcase datasets, benchmarks, and tools like Xet. - Highlight use cases like efficient large dataset updates, Parquet editing, and deduplication to demonstrate the Hub’s value for data workflows. - Create demos, benchmarks, and tools (e.g., Colab notebooks) to illustrate best practices for data storage and versioning.bExperiment with Xet, Parquet, and other data formats to showcase their potential for ML and data engineering. - Produce high-quality tutorials, blog posts, and videos that make complex topics accessible. - Share insights on storage optimization, dataset versioning, and deduplication to empower developers. - Actively participate in online communities (Discord, GitHub, forums) to highlight contributions, answer questions, and foster collaboration. - Ensure datasets and tools released on the Hub are well-documented, with clear examples, benchmarks, and use cases. About you You’re a great fit if you: - Have strong technical skills in Python, data libraries (e.g., pandas, pyarrow, huggingface/datasets), and storage systems (Parquet, Open Table Formats, S3). - Are a hands-on builder who loves experimenting with data tools, storage optimization, and dataset versioning. - Can clearly explain complex topics (e.g., deduplication, compression, Parquet editing) through writing, demos, or talks. - Are active in developer communities (GitHub, Discord, forums) and passionate about open source and knowledge sharing. - Thrive in fast-moving environments and enjoy building in public to inspire others. If you're interested in joining us but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and backgrounds complement one another. We're happy to consider where you might be able to make the biggest impact. More about Hugging Face We are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where you feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community, as well as the future of machine learning more broadly. Hugging Face is an equal opportunity employer, and we do not discriminate based on race, ethnicity, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or ability status. We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to grow continuously. We provide all employees with reimbursement for relevant conferences, training, and education. We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off. We support our employees wherever they are. While we have office spaces in NYC and Paris, we're very distributed, and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed. We want our teammates to be shareholders. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside.

• Code SQL for both on-premises and Azure SQL environments, including optimizing queries, data manipulation, and ensuring high performance. • Design and build data pipelines from various data sources (mainly databases) to a target Data Warehouse / Lakehouse, using batch data load strategies, and leveraging Microsoft Azure technologies (Data Factory and Databricks). • Implement data validation and quality assurance checks to maintain high data integrity standards, ensuring accurate and reliable data for analytical purposes. • Collaborate with a cross-functional team of business analysts, architects, engineers, data analysts, and key stakeholders to formulate and implement data pipelines. • Participate in architecture discussions, take ownership and responsibility over new projects or requirements, and perform audits to ensure data quality and integrity. • Utilize programming languages, databases & cloud Data Warehouses / Lakehouses. • Document database designs that include data models, metadata, ETL specifications, and process flows for business data project integrations. • Monitor, maintain, and optimize production systems. • Investigate and resolve incidents reported by users. • Identify opportunities to automate, consolidate, and simplify data solutions at scale. • Define best practices and create coding standards and patterns that can be reused by the rest of the data engineers.