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Cargill is a family company committed to providing food and agricultural solutions to nourish the world in a safe, responsible, and sustainable way. We sit at the heart of the supply chain, partnering with producers and customers to source, make and deliver products that are vital for living. By providing customers with life’s essentials, we enable businesses to grow, communities to prosper, and consumers to live well. This position is in our Food Enterprise where we are committed to serving food manufacturers, food service customers, and retailers with a complete range of innovative ingredients and branded products. Our portfolio includes poultry, beef, egg, alternative protein, salt, oils, starches, sweeteners, cocoa and chocolate. Job Purpose and Impact The Senior Professional, Data Engineering job designs, builds and maintains complex data systems that enable data analysis and reporting. With minimal supervision, this job ensures that large sets of data are efficiently processed and made accessible for decision making. Within Food Data Engineering Americas, this role builds and operates data products on Cargill’s Minerva platform, supporting the ongoing migration off CDP and enabling scalable, governed data solutions for Supply Chain, Procurement & Manufacturing, Commercial Excellence, and LATAM domains. Key Accountabilities Data Infrastructure: Prepares data infrastructure to support the efficient storage and retrieval of data. Data Formats: Examines and resolves appropriate data formats to improve data usability and accessibility across the organization. Data & Analytical Solutions: Develops complex data products and solutions using advanced engineering and cloud-based technologies, ensuring they are designed and built to be scalable, sustainable and robust. Data Pipelines: Develops and maintains streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move it to data stores like data lake, data warehouse and others. Data Systems: Reviews existing data systems and architectures to identify areas for improvement and optimization. Stakeholder Management: Collaborates with multi-functional data and advanced analytic teams to gain requirements and ensure that data solutions meet the functional and non-functional needs of various partners. Data Frameworks: Builds complex prototypes to test new concepts and implements data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives. Automated Deployment Pipelines: Develops automated deployment pipelines improving efficiency of code deployments with fit-for-purpose governance. Data Modeling: Performs complex data modeling in accordance with the datastore technology to ensure sustainable performance and accessibility. Minerva Data Product Delivery: Builds and maintains data products within the Minerva Engineering Framework (MEF), supporting migration of workloads and historical data from CDP to Minerva’s Lakehouse and Compute account architecture. Migration Validation: Validates migrated data products using platform reconciliation tooling, ensuring row counts, schema, and aggregate accuracy between legacy (CDP/Impala) and Minerva (AWS Athena/Lakehouse) sources. Qualifications Minimum requirement of 4 years of relevant work experience. Typically reflects 5 years or more of relevant experience Preferred Qualifications: Cloud Environments: Experience developing data systems on major cloud platforms (AWS, GCP, Azure). Hands-on AWS experience strongly preferred given Minerva’s AWS-native architecture. Data Architecture: Hands-on experience building modern data architectures, including data lakes, data lakehouses, and data hubs, along with related capabilities such as ingestion, governance, modeling, and observability. 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 (Confluent Kafka, Apache Flink). Data Modeling: Expertise in data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow/Astronomer). Deep experience with modeling concepts like SCD and schema evolution. Data Transformation: Strong background using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI. Programming: Advanced programming skills in Python, Java, Scala, or similar languages. Expert-level proficiency in SQL for data manipulation and optimization. DevOps: Demonstrated experience in DevOps practices, including code management, CI/CD, and deployment strategies. Data Governance: Strong background in data governance principles, including data quality, privacy, and security considerations for data product development and consumption. Snowflake & Information Warehouse: Working knowledge of Snowflake, including warehouse/database/schema design and the dbt-Snowflake adapter, for provisioning and serving Minerva data products in the Information Warehouse layer. Platform Migration Experience: Prior experience migrating on-premises or legacy platform workloads (e.g., Cloudera, Hadoop, Impala) to a cloud-native lakehouse, including data reconciliation and historical/CDC data loads. Engineering Framework & Tooling: Familiarity with Git-based CI/CD delivery for data products (e.g., Vela CI/CD or similar pipeline tooling), Data Product Package structures, and metadata/catalog tools (e.g., Atlan) for lineage and discovery. Domain & Source Systems: Exposure to SAP source systems and food/CPG domain data (e.g., supply chain, procurement, commercial/sales) is a plus, given FDEAMR’s portfolio scope. Compensation Data The expected salary for this position is $90,000 - $155,000. Compensation varies depending on a wide array of factors including but not limited to the specific location, certifications, education, and level of experience. The disclosed range estimate may be adjusted for any applicable geographic differential associated with the location at which the position may be filled. This position is eligible for a discretionary incentive award. The incentive award amount is dependent upon company performance and your personal performance. At Cargill we put people first. As part of your overall rewards, we offer a comprehensive benefit program including medical and/or other benefits dependent on the position offered and hours worked. Visit: https://www.cargill.com/page/my-health/mh-health-and-wellnessto learn more (subject to certain collective bargaining agreements for Union positions) . Minnesota Sick and Safe Leave accruals of one hour for every 30 worked, up to 48 hours per calendar year unless otherwise provided by law Equal Opportunity Employer, including Disability/Vet
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.