.As a Sr. Cloud Data Infrastructure Engineer with Intuitive, you will be responsible for building or converting legacy data pipelines from legacy environments to modern cloud environments to help the analytics and data science initiatives across our enterprise customers. You will be working closely with SMEs in Data Engineering and Cloud Engineering, to create solutions and extend Intuitive's DataOps Engineering Projects and Initiatives. The Sr. Cloud Data Infrastructure Engineer will be a central critical role for establishing the DataOps/DataX data logistics and management for building data pipelines, enforcing best practices, ownership for building complex and performant Data Lake Environments, work closely with Cloud Infrastructure Architects and DevSecOps automation teams. The Sr. Cloud Data Infrastructure Engineer is the main point of contact for all things related to DataLake formation and data at scale. In this role, we expect our DataOps leaders to be obsessed with data and providing insights to help our end customers.**ROLES & RESPONSIBILITIES**:- Design, develop, implement, and tune large-scale distributed systems and pipelines that process large volume of data; focusing on scalability, low-latency, and fault-tolerance in every system built.- Developing scalable and re-usable frameworks for ingesting large data from multiple sources.- Modern Data Orchestration engineering - query tuning, performance tuning, troubleshooting, and debugging big data solutions.- Provides technical leadership, fosters a team environment, and provides mentorship and feedback to technical resources.- Deep understanding of ETL/ELT design methodologies, patterns, personas, strategy, and tactics for complex data transformations.- Data processing/transformation using various technologies such as spark and cloud Services.- Understand current data engineering pipelines using legacy SAS tools and convert to modern pipelines.**Data Infrastructure Engineer Strategy Objectives: End to End Strategy**Define how data is acquired, stored, processed, distributed, and consumed. Collaboration and Shared responsibility across disciplines as partners in delivery for progressing our maturity model in the End-to-End Data practice.- Understanding and experience with modern cloud data orchestration and engineering for one or more of the following cloud providers - AWS, Azure, GCP.- Leading multiple engagements to design and develop data logistic patterns to support data solutions using data modeling techniques (such as file based, normalized or denormalized, star schemas, schema on read, Vault data model, graphs) for mixed workloads, such as OLTP, OLAP, streaming using any formats (structured, semi-structured, unstructured).- Understanding of one or more is a big plus -CI/CD, cloud devops, containers (Kubernetes/Docker, etc.), Python/PySpark/JavaScript
