14 days ago
Atlanta, GA, USASenior
Responsibilities
- Own and evolve the data platform architecture across Azure Data Factory, Azure Blob Storage, Azure SQL Server, and on-premises ETL and data-acquisition components.
- Design, build, and maintain reliable ETL/ELT pipelines between on-premises source systems and Azure.
- Analyze and optimize query performance through indexing, execution-plan analysis, and workload tuning.
- Manage database schemas, migrations, maintenance, and production performance issues using zero-downtime practices.
- Own database and pipeline deployment processes, including version control, peer review, audited rollouts, and rollbacks.
- Monitor platform health, pipeline success rates, resource usage, and costs while driving capacity and cost optimization.
- Design and build Apache Superset dashboards that are automatically mapped and embedded for customers.
- Monitor platform capabilities, including automated and AI-driven features, and contribute to automation and self-healing service scaling.
- Integrate and evaluate emerging data and AI technologies, including Model Context Protocol, Superset MCP, and Azure AI Foundry readiness.
- Make technical and architectural decisions in a complex enterprise environment and mentor other data professionals.
Requirements
- 5–6 years of experience designing, building, and supporting enterprise data platforms.
- Bachelor’s degree in Computer Science, Data Engineering, Information Technology, or a related field, or equivalent practical experience.
- Strong hands-on experience with Azure Data Factory, Azure Blob Storage, and Azure SQL Server.
- Advanced T-SQL proficiency, including query optimization, execution-plan analysis, indexing strategies, stored procedures, tables, and views.
- Experience building ETL/ELT pipelines integrating on-premises systems with Azure cloud environments.
- Experience managing database schema changes, migrations, maintenance, and production performance issues.
- Experience implementing deployment practices for database and data-pipeline changes, including version control, peer review, deployment, and rollback processes.
- Strong understanding of data modeling, data quality, platform monitoring, access controls, audit logging, and secure deployment practices.
- Ability to independently make technical and architectural decisions in a complex enterprise environment.
- Strong communication and stakeholder-management skills, including collaboration across global teams and mentoring data professionals.
Tech Stack
Apache Superset
Categories
Data Engineering
About Cargill
Cargill builds and operates global food and agriculture supply chains, selling commodities, food ingredients, animal nutrition, and risk-management services to food manufacturers, retailers, and farmers. It trades and processes grains, oilseeds, cocoa, and proteins, and provides logistics and biofuel solutions. Founded in 1865 and headquartered in Wayzata, Minnesota, Cargill is privately held and family-owned.