Senior Data Engineer - Ag & Trading
Cargill (company site, opens in a new tab)8+ yrsBengaluruOn-siteDirect career pageEst. 22–35 LPA
Posted 21h ago
Reach people at Cargill
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Cargill you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, build and maintain complex scalable data platforms and pipelines on AWS supporting batch and real-time workloads for decision making.
- Develop and optimize data products, automated deployment pipelines, and data models to ensure performance, sustainability, and cost efficiency.
- Collaborate with cross-functional data teams and business stakeholders to gather requirements and deliver robust data engineering solutions including GenAI data enablement.
Minimum Requirements
- Minimum 8 years of relevant data engineering experience.
- Strong expertise in AWS ELT data platform design, advanced Python engineering, Snowflake and relational databases (Oracle, SQL Server, PostgreSQL).
- Experience building batch and streaming data pipelines using Apache Kafka, with emphasis on reliability, performance and cost optimization.
- Experience with data security, governance, role-based access controls, and data quality frameworks.
Ideal Candidate Profile
- Experienced in large-scale data platform design specifically for trading or finance industry use cases.
- Able to independently develop production-grade Python pipelines and manage complex data systems with minimal supervision.
- Familiar with advanced Snowflake data modeling, query optimization, and cloud data engineering best practices combining data infrastructure with GenAI enablement.