Sr. Data Engineer

Cargill is committed to providing food and agricultural solutions to nourish the world in a safe, responsible, and sustainable way. Sitting at the heart of the supply chain, we partner with farmers and customers to source, make and deliver products that are vital for living.
Our 155,000 team members innovate with purpose, providing customers with life's essentials so businesses can grow, communities prosper, and consumers live well. With over 160 years of experience as a family company, we look ahead while remaining true to our values. We put people first. We reach higher. We do the right thing-today and for generations to come.

Job Purpose and Impact

• • The Senior Data Engineer will be a core contributor to the architecture, delivery, and operations of the Company's internal business intelligence and data platform, which delivers automated ETL, ad-hoc and automated analytics, and embedded dashboards to users across the enterprise. The successful candidate will own day-to-day data engineering architecture, accelerate feature delivery and performance tuning, and mentor the team's data professionals to ensure the platform scales sustainably with enterprise demand.

Key Accountabilities

• • Owns and evolves the architectural design of the data platform - Azure Data Factory, Azure Blob Storage, Azure SQL Server, and on-premises ETL/data-acquisition components - maintaining architecture standards, decision records, and consistency of data feeds, models, and pipeline design.
  • Analyzes and optimizes query performance, including indexing strategy, execution-plan analysis, and tuning of slow workloads; leads break-fix for performance degradation.
  • Designs, builds, and maintains ETL/ELT pipelines spanning on-premises source systems to Azure, with an emphasis on reliability, idempotency, and data quality.
  • Manages database lifecycle operations - schema changes, migrations, and maintenance with a zero-downtime discipline - and delivers feature work to clear and prevent delivery backlogs.
  • Owns CI/CD for database and pipeline deployments, including version control, peer review, and audited rollouts and rollbacks; monitors platform health, pipeline success rates, and resource/cost consumption, driving capacity and cost optimization.
  • Designs and builds Apache Superset dashboards, balancing dashboard authoring with architectural priorities; dashboards are auto-mapped and embedded for customers via the platform's tagging system, eliminating manual publishing work.
  • Monitors the platform on behalf of customers, including automated and AI-driven features, to ensure capabilities behave reliably; contributes to automation and self-healing service scaling to minimize manual intervention, shifting toward quality, governance, and monitoring of AI-generated outputs as generative-AI dashboard creation matures.
  • Contributes to integrating AI capabilities into the platform - including Model Context Protocol (MCP), Superset MCP, and Azure AI Foundry readiness - and evaluates emerging data and AI technologies for adoption.
  • Collaboration, Mentoring & Security practices

Qualifications

• • 5-6 years experience 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.
  • Significant professional experience designing, building, and supporting enterprise data platforms.
  • Strong hands-on experience with Azure Data Factory, Azure Blob Storage, and Azure SQL Server.
  • Advanced proficiency in T-SQL, including query optimization, execution-plan analysis, indexing strategies, stored procedures, tables, and views.
  • Demonstrated experience building reliable ETL/ELT pipelines that integrate on-premises systems with Azure cloud environments.
  • Experience managing database schema changes, migrations, maintenance, and production performance issues.
  • Experience implementing CI/CD practices for database and data-pipeline deployments, 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, with the ability to collaborate across global teams and mentor other data professionals.

Equal Opportunity Employer, including Disability/Vet.

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