Platform Engineer

The Platform Engineer will play a key role in managing and evolving the organization’s enterprise data platform. This position will oversee cloud data warehouse environments, develop scalable data ingestion and transformation workflows, and help drive data platform strategy. The engineer will also support the integration of AI capabilities and ensure the platform remains secure, reliable, and optimized for analytics, business intelligence, and AI applications., n \n

• Serve as a primary administrator for the enterprise cloud data warehouse, managing user access, roles, security policies, configurations, and overall platform operations across multi-cloud or hybrid environments. \n

• Monitor and optimize platform performance, security, reliability, and costs while ensuring operational SLAs are consistently maintained. \n

• Design, develop, and maintain scalable data pipelines using CDC, ETL/ELT, API integrations, and batch/file-based ingestion methods to ensure reliable and timely data delivery. \n

• Develop and maintain curated datasets, semantic views, and business data models that support reporting, visualization, analytics, and AI-driven applications. \n

• Work closely with data owners, engineering teams, and analytics stakeholders to onboard new data sources, establish ingestion standards, and maintain data lineage and documentation. \n

• Assist with the deployment, testing, and monitoring of AI-enabled solutions, including LLMs and AI agents such as ChatGPT, Copilot, and Snowflake Cortex, ensuring effective integration with enterprise data workflows. \n

• Collaborate with architecture and data teams on semantic layers, knowledge graphs, and ontology initiatives that improve data accessibility and support reliable AI-driven querying. \n

• Create and maintain technical documentation covering platform architecture, operational procedures, security standards, and best practices; provide guidance and support to both technical and business users. \n

• Research and evaluate emerging data platform capabilities, including Private Preview features, and help drive the adoption of relevant technologies into production environments. \n, * Experience supporting enterprise AI implementations and integrating AI capabilities into existing data platforms.

• Bachelor’s degree in Computer Science, Information Systems, or a related technical discipline; Master’s degree preferred. \n

• 5+ years of experience in data engineering, data platform administration, or enterprise BI, including at least 2 years administering cloud data warehouse platforms such as Snowflake, Redshift, or similar technologies at scale. \n

• Hands-on experience supporting multi-account or multi-tenant cloud data environments, including access management, security controls, and platform reliability. \n

• Strong SQL skills with experience using Python, Bash, or other scripting languages for automation and data transformation. \n

• Experience with data ingestion and ETL/ELT technologies such as Fivetran, HVR, Matillion, dbt, Coalesce, and Postman. \n

• Familiarity with AI and LLM technologies including ChatGPT, Claude, Microsoft Copilot, and Snowflake Cortex, along with an understanding of agent-based architectures and integrations. \n

• Snowflake certification such as SnowPro Core or Advanced, or an equivalent cloud/data platform certification. \n

• Understanding of Agile development methodologies, DevOps practices, and CI/CD processes. \n

• Strong communication and collaboration skills, with the ability to work effectively across engineering, architecture, analytics, and business teams. \n, * Experience with BI and visualization tools such as Tableau, Power BI, or Sigma. \n

• Knowledge of agent-based data architectures, MCP servers, and agent-to-agent communication protocols. \n

• Experience with data governance, data quality, metadata management, and data lineage practices. \n

• Familiarity with cloud security frameworks and best practices surrounding enterprise data privacy.