Principal, Data Engineering

cargill

1d•Onsite

About The Position

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 SummaryThe Principal, Data Engineering job provides thought leadership in the execution of strategic plans related to design, development and maintenance of robust data systems. As a recognized subject matter expert in the field, this job leads the development of efficient processing and availability of data for analysis and reporting.

Requirements

Minimum requirement of 6 years of relevant work experience.
Typically reflects 10 years or more of relevant experience.

Nice To Haves

ARCHITECTURAL LEADERSHIP: Defines long-term technical direction, establishes best practices, and ensures solutions are scalable, maintainable, and aligned with enterprise strategy.
OPERATIONAL EXCELLENCE MINDSET: Champions reliability, observability, and performance, ensuring data systems meet high standards for availability and quality.
STRATEGIC DECISION MAKING: Evaluates competing priorities such as speed, cost, risk, and flexibility to make sound technical and architectural decisions.
CLOUD DATA PLATFORMS: deep expertise implementing cloud-based data warehouses, data lakes, and open table formats in large-scale production environments. Has hands-on experience with technologies such as Snowflake, AWS, and open lake house ecosystems.
DATA INGESTION: Demonstrated proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet)
DATA STREAMING: Experience developing data pipelines with streaming architectures and tools (Kafka, Flink).
DATA TRANSFORMATION: Strong background with using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI.
DEVOPS: Extensive experience in DevOps practices, including code management, CI/CD, and deployment strategies.

Responsibilities

DATA PIPELINES: Provides thought leadership on the design and development of data pipelines that facilitate the movement of data from various sources to internal databases.
DATA INFRASTRUCTURE: Influences the construction and optimization of data infrastructure, providing appropriate data formats to ensure data readiness for analysis.
DATA FORMATS: Examines and sees improvement opportunities for appropriate data formats to optimize data usability and accessibility across the organization.
STAKEHOLDER MANAGEMENT: Cultivates positive relationships and partners to understand data needs and encourage alignment with organizational objectives.
DATA SYSTEMS: Develops and guides the implementation of data products and solutions using advanced engineering and cloud-based technologies, ensuring they are designed and built to be scalable, sustainable, and robust.
SOLUTIONS DEVELOPMENT: Leads efforts to improve the development of technical products and solutions driving big data and cloud-based technologies, ensuring they are designed and built to be scalable, sustainable, and robust.
DATA FRAMEWORKS: Drives development standards and brings forward prototypes to test new data framework concepts and architecture patterns supporting efficient data processing and analysis and promoting standard methodologies in data management.
AUTOMATED REPORTING SYSTEMS: Builds automated reporting systems that provide timely insights and facilitate data driven decision making.
DATA MODELING: Provides thought leadership on data modeling and preparation of data in databases for use in various analytics tools and to develop data pipelines to move and improve data assets.

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