Data Platform Engineer

As a Data Platform Engineer at CodeRound AI, you will play a crucial role in designing, building, and scaling the core data infrastructure that drives analytics, AI/ML systems, and business intelligence platforms for enterprise-scale environments. You will be working on distributed data systems, large-scale processing pipelines, and cloud-native infrastructure to handle high-volume real-time and batch data workloads. This position is perfect for individuals who enjoy tackling large-scale data engineering challenges, constructing reliable platform infrastructure, and thriving in high-growth AI-driven startup settings. Key Responsibilities: - Design and maintain scalable data platform infrastructure - Build reliable and efficient data services for analytics and AI/ML workflows - Contribute to architecture and technical decision-making for platform scalability - Develop and optimize batch and real-time data pipelines - Create scalable ETL/ELT workflows for large-scale datasets - Manage distributed data processing systems and streaming architectures - Work with cloud-native infrastructure on AWS and GCP - Deploy and manage scalable distributed systems using Kubernetes - Monitor, troubleshoot, and enhance production data systems - Ensure platform reliability, security, and operational excellence - Collaborate with data scientists, analysts, and backend engineering teams - Support AI/ML and business intelligence initiatives through scalable infrastructure - Automate infrastructure, deployment workflows, and operational processes Qualifications Required: - 5+ years of experience in Data Engineering or Data Platform Engineering - Proficiency in Python, Scala, and SQL - Hands-on experience with Apache Spark, Kafka, and Airflow - Understanding of distributed systems and large-scale data processing architectures - Experience with building ETL/ELT workflows and real-time data pipelines - Familiarity with cloud platforms like AWS or GCP - Comfortable with Kubernetes, infrastructure automation, and platform scalability - Experience with Databricks and modern big data tooling - Ability to write clean, maintainable, and production-ready code - Prior startup experience is preferred Join CodeRound AI to be part of a team that is revolutionizing the way tech talent is matched with fast-growing startups. You will have the opportunity to work on cutting-edge technologies, solve complex data engineering challenges, and contribute to building large-scale data platforms that power AI and enterprise intelligence. Additionally, you will enjoy high ownership in a Gartner-recognized AI startup, collaborate with strong engineering, AI/ML, and product leadership, and play a key role in scaling infrastructure for next-generation supply chain intelligence systems. As a Data Platform Engineer at CodeRound AI, you will play a crucial role in designing, building, and scaling the core data infrastructure that drives analytics, AI/ML systems, and business intelligence platforms for enterprise-scale environments. You will be working on distributed data systems, large-scale processing pipelines, and cloud-native infrastructure to handle high-volume real-time and batch data workloads. This position is perfect for individuals who enjoy tackling large-scale data engineering challenges, constructing reliable platform infrastructure, and thriving in high-growth AI-driven startup settings. Key Responsibilities: - Design and maintain scalable data platform infrastructure - Build reliable and efficient data services for analytics and AI/ML workflows - Contribute to architecture and technical decision-making for platform scalability - Develop and optimize batch and real-time data pipelines - Create scalable ETL/ELT workflows for large-scale datasets - Manage distributed data processing systems and streaming architectures - Work with cloud-native infrastructure on AWS and GCP - Deploy and manage scalable distributed systems using Kubernetes - Monitor, troubleshoot, and enhance production data systems - Ensure platform reliability, security, and operational excellence - Collaborate with data scientists, analysts, and backend engineering teams - Support AI/ML and business intelligence initiatives through scalable infrastructure - Automate infrastructure, deployment workflows, and operational processes Qualifications Required: - 5+ years of experience in Data Engineering or Data Platform Engineering - Proficiency in Python, Scala, and SQL - Hands-on experience with Apache Spark, Kafka, and Airflow - Understanding of distributed systems and large-scale data processing architectures - Experience with building ETL/ELT workflows and real-time data pipelines - Familiarity with cloud platforms like AWS or GCP - Comfortable with Kubernetes, infrastructure automation, and platform scalability - Experience with Databricks and modern big data tooling - Ability to write clean, maintainable, and production-ready code - Prior startup experience is pr