Sr. Data Engineer

Cargill•Atlanta, GA

22h

About The Position

The Senior Data Engineer will be a core contributor to the architecture, delivery, and operations of Cargill’s internal business intelligence and data platform. The platform provides automated ETL, ad-hoc and automated analytics, and embedded dashboards to enterprise users. This role owns day-to-day data engineering architecture, accelerates feature delivery and performance tuning, and mentors data professionals to help the platform scale sustainably with enterprise demand. The position spans Azure Data Factory, Azure Blob Storage, Azure SQL Server, and on-premises ETL and data-acquisition components. It includes building reliable data pipelines, managing database lifecycle and deployment processes, monitoring platform health and cost, and designing embedded Apache Superset dashboards. The role also contributes to platform automation and integration of AI capabilities, including MCP, Superset MCP, and Azure AI Foundry readiness.

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 proficiency in T-SQL, including query optimization, execution-plan analysis, indexing strategies, stored procedures, tables, and views.
Demonstrated experience building reliable ETL/ELT pipelines integrating 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 make technical and architectural decisions independently in a complex enterprise environment.
Strong communication and stakeholder-management skills, including the ability to collaborate across global teams and mentor data professionals.

Responsibilities

Own and evolve data-platform architecture, maintaining architecture standards, decision records, and consistency of data feeds, models, and pipeline design.
Analyze and optimize query performance using indexing strategies, execution-plan analysis, and tuning of slow workloads; lead break-fix efforts for performance degradation.
Design, build, and maintain ETL/ELT pipelines between on-premises source systems and Azure, emphasizing reliability, idempotency, and data quality.
Manage database lifecycle operations, including schema changes, migrations, and maintenance with a zero-downtime discipline.
Deliver feature work to clear and prevent delivery backlogs.
Own CI/CD for database and pipeline deployments, including version control, peer review, audited rollouts, and rollbacks.
Monitor platform health, pipeline success rates, and resource and cost consumption; drive capacity and cost optimization.
Design and build Apache Superset dashboards, balancing dashboard authoring with architectural priorities; support automated mapping and embedding through the platform’s tagging system.
Monitor the platform on behalf of customers, including automated and AI-driven features, to ensure capabilities operate reliably.
Contribute to automation and self-healing service scaling to minimize manual intervention, with a focus on quality, governance, and monitoring of AI-generated outputs as generative-AI dashboard creation matures.
Integrate AI capabilities into the platform, including MCP, Superset MCP, and Azure AI Foundry readiness, and evaluate emerging data and AI technologies for adoption.
Collaborate across global teams and mentor other data professionals.