4+ years of relevant work experience, typically 5+ years preferred.
Experience with major cloud platforms (AWS, GCP, Azure).
Hands-on experience building modern data architectures (data lakes, lakehouses, hubs).
Proficiency in data ingestion tools (Kafka, AWS Glue) and storage formats (Iceberg, Parquet).
Expertise in data transformation and modeling using SQL frameworks (dbt, AWS Glue, Airflow).
Strong background in using Spark for data transformation and performance tuning.
Advanced programming skills in Python, Java, Scala, or similar languages.
Responsibilities
Prepare data infrastructure for efficient storage and retrieval.
Examine and resolve data formats to enhance usability and accessibility.
Develop complex data products using advanced engineering and cloud technologies.
Maintain streaming and batch data pipelines for seamless data ingestion.
Review existing data systems to identify improvement areas.
Collaborate with multi-functional teams to gather requirements for data solutions.
Build prototypes to test new concepts and implement data engineering frameworks.
Benefits
Eligible for a discretionary incentive award based on company and personal performance.
Accrual of Minnesota Sick and Safe Leave at one hour for every 30 worked, up to 48 hours per year.
Full Job Description
Job Purpose and Impact
The Senior Data Engineer 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.
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 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 to the datastore technology to ensure sustainable performance and accessibility.
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).
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 (Kafka, Flink).
DATA MODELING: Expertise in data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow). Deep experience with modeling concepts like SCD and schema evolution.
DATA TRANSFORMATION: Strong background with 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.
Previous Cargill experience, or experience with Animal Health Nutrition
The business will not sponsor work visas for applicants for this position.
Additional Applicant Information
The expected salary for this position is $107,000-160,000. 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.
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.
