Data Engineer

This Data Platform Engineer is responsible for overseeing enterprise data platform architecture, managing cloud data warehouse environments, and building data ingestion workflows. The role shapes the organization’s data strategy by implementing efficient pipelines, integrating AI capabilities, and maintaining platform integrity to enable advanced analytics, AI applications, and business intelligence tools at scale. Key Responsibilities Act as the primary administrator for the enterprise cloud data warehouse environment, managing user access, roles, security policies, and platform configurations across multi-cloud or hybrid environments. Monitor, tune, and optimize data platform performance, security, and cost management, ensuring SLAs are met and platform reliability is maintained. Design, develop, and support scalable data pipelines using a variety of ingestion techniques such as CDC, ETL/ELT processes, API integration, and batch/file-based loads, ensuring data quality and timeliness. Build and maintain curated data sets, semantic data views, and business data models that serve reporting, visualization, and AI-based applications. Collaborate with data source owners and analytics teams to onboard new data sources, define ingestion standards, and document data lineage for governance and troubleshooting. Support the deployment, testing, and monitoring of AI-enabled applications, including Large Language Models (LLMs) and AI agents (e.g., ChatGPT, Copilot, Cortex), ensuring seamless integration with data workflows. Partner with architecture teams on semantic layers, knowledge graphs, and ontology efforts to support reliable AI query layers and data accessibility. Document platform architecture, best practices, and operational procedures; provide training and support to technical and non-technical users. Evaluate new platform capabilities, including Private Preview features, and lead adoption strategies to incorporate innovations into production. Core Qualifications & Requirements Bachelor’s Degree in Computer Science, Information Systems, or related technical field; Master’s Degree preferred. 5+ years of experience in data engineering, data platform administration, or enterprise BI, with at least 2 years managing cloud data warehouses such as Snowflake, Redshift, or similar at scale. Hands-on experience with multi-account or multi-tenant cloud data architectures, managing access controls, security, and platform stability. Proficient in SQL programming and scripting languages for data transformation and automation. Strong experience with data ingestion tools and ETL/ELT frameworks including Fivetran, HVR, Matillion, dbt, Apache Coalesce, and Postman. Familiarity with AI and LLM tools such as ChatGPT, Claude, Microsoft Copilot, and Snowflake Cortex, with understanding of agent-based architecture and integrations. Certified in Snowflake (SnowPro Core or Advanced) or comparable platform certifications. Knowledge of agile development practices, DevOps principles, and CI/CD workflows. Excellent written and verbal communication skills with the ability to collaborate across technical and business teams. Nice-to-Have Qualifications Experience with BI and visualization platforms like Tableau, Power BI, or Sigma. Understanding of agent-based data architectures, MCP servers, and agent-to-agent communication protocols. Experience with data governance, data quality, and metadata management tools. Working knowledge of cloud security standards and best practices for data privacy. Familiarity with enterprise AI deployment and integration strategies. Core Technical Skills Cloud Data Warehousing (Snowflake, Redshift, Azure Synapse) SQL, Python, Bash scripting AI/LLM tools (ChatGPT, Claude, Copilot) and agent architectures Data modeling, semantic layers, ontology, knowledge graphs Data governance, security policies, multi-cloud deployment #J-18808-Ljbffr