• Lead and deliver modern data platforms and analytics initiatives for global clients. • Blend hands-on data engineering, solution architecture, and client advisory responsibilities. • Work closely with business and technical stakeholders to design scalable, secure, and high-performance data solutions.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a Principal Data & AI/ML Ops Engineer at Smartsheet, you will have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform. - Data Architecture and Design: Designing and overseeing the architecture of scalable and reliable data platforms, including data pipelines, storage solutions, and processing systems. - Data Modelling and Management: Developing and implementing data models, ensuring data quality, and establishing data governance policies. - Data Pipeline Development: Building and optimising data pipelines for ingesting, processing, and transforming large datasets from various sources. - Performance Optimisation: Identifying and resolving performance bottlenecks in data pipelines and systems, ensuring efficient data retrieval and processing. - Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure. - Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform. - Data Security and Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data. - Perform other duties as assigned. Qualifications - Enterprise SaaS software solutions with high availability and scalability. - Solution handling large scale structured and unstructured data from varied data sources. - Experience in building and maintaining data platform systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure ensuring scalability, reliability, efficiency and security. - Working with Product engineering team to influence designs with data, AI and analytics use cases in mind. - In depth experience in System design involving large Petabytes of data with Databricks Lakehouse. - Experience in modern AI/Data infrastructure patterns, Semantics layer Organizing data for AI agents (metadata, context). - AI/MLOps workflows on Databricks, MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph. - Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration. - Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable. - Programming languages like Python, SQL, and potentially Java or Scala. - Exposure to Snowflake and Data pipeline frameworks like Airbyte/Airflow is preferable. - Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting. - Solution Cost Optimisations and design to cost. - Driving engineering excellence initiatives. - Legally eligible to work in India on an ongoing basis. Benefits - Your ideas are heard, your potential is supported, and your contributions have real impact. - You’ll have the freedom to explore, push boundaries, and grow beyond your role. - We welcome diverse perspectives and nontraditional paths. Equal Opportunity Employer Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, and India. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.

• Responsible for designing, building, and maintaining scalable data pipelines to support Square team’s data and feature engineering needs • Lead feature data preparation, transformation, validation, and monitoring for machine learning and recommendation/search systems • Collaborate closely with algorithm, product, and business teams to translate business requirements into reliable data and feature solutions • Drive the development of core data infrastructure and feature platforms with data-driven strategies to maximize business impact • Ensure data quality, stability, and performance across offline and online feature pipelines • Identify data gaps and optimization opportunities, and define success metrics together with Product and Business stakeholders

• Design, develop, and maintain scalable, reliable data pipelines on GCP to support Analytics, Machine Learning, and AI initiatives. • Build and orchestrate data ingestion, transformation, and processing pipelines using Python, Jupyter Notebooks, and Dataproc. • Prepare, organize, and make data available for Machine Learning and Generative AI models. • Work with BigQuery for data analysis, transformation, modeling, and performance optimization. • Support the development of Generative AI applications by integrating data into solutions based on LangChain, Google ADK, and Vertex AI. • Manage data artifacts, models, and experiments using Artifact Registry. • Use Google Vector Database for solutions involving embeddings, semantic search, and RAG (retrieval-augmented generation) use cases. • Version code, pipelines, and notebooks using GitLab, following engineering best practices. • Collaborate with multidisciplinary teams in an agile environment, supporting data-driven architectures. • Contribute to data governance, quality, security, and observability practices.