Advanced Analytics Data Engineer

Corteva Agriscience•Midland, TX

About The Position

The Advanced Analytics Data Engineer is a key contributor to the organization’s data-driven initiatives, responsible for building and maintaining data pipelines, enabling advanced analytics, and supporting continuous improvement. This role collaborates with cross-functional teams to deliver high-quality data solutions that drive business impact.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience in programming/data engineering.
Proven programming experience (Python, VBA preferred).
Experience in customer/end-user facing roles.
Strong technical skills in data visualization, management, and automation.
Familiarity with Microsoft Power Platform, Azure Data Factory, Databricks/Unity Catalog, SQL, and PowerBI dashboard creation.
Project management skills and a high degree of ownership.

Nice To Haves

Hands-on experience with cloud-based data platforms (Azure, AWS, Google Cloud).
Experience with big data technologies (Spark, Hadoop, Kafka).
Knowledge of data modeling, ETL processes, and data integration.
Familiarity with data governance, security best practices, and real-time data streaming.
Experience in agriculture or manufacturing environments is a plus.

Responsibilities

Acquire, cleanse, and prepare data for analysis, ensuring data quality and accessibility for modeling and reporting.
Design, implement, and maintain scalable data pipelines and architectures (including data lakes and warehouses) tailored to analytical and business needs.
Integrate advanced analytics and machine learning models into the data architecture, overseeing ongoing model and data maintenance.
Build and support custom data visualizations and tools, troubleshooting issues and optimizing performance along the data pipeline.
Manage the end-to-end lifecycle of software and data applications, from requirements gathering to deployment and support.
Collaborate with manufacturing clients, digital translators, and data scientists to define requirements and deliver custom data solutions.
Partner with IT and enterprise architecture teams to identify and resolve data-related roadblocks.
Champion automation and continuous improvement methodologies (Lean, Six Sigma), integrating them with data engineering projects.
Train customers and end users on developed tools and dashboards, ensuring effective adoption and utilization.
Work across multiple time zones and with diverse stakeholders, demonstrating flexibility and strong communication skills.