Data Engineer

Agile Defense • Omaha, NE

1d • Onsite

About The Position

Design, build, and maintain highly scalable, reliable, and efficient data pipelines for extracting, transforming, and loading (ETL/ELT) data from various sources into data warehouses, data lakes, and other storage systems. Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and ensure the efficient flow of data across various platforms and systems. Develop, deploy, and maintain real-time data streaming solutions to process and deliver data with low latency (using tools like Apache Kafka, Flink, or Spark Streaming). Optimize and automate data workflows, ensuring that data pipelines are efficient, reliable, and capable of handling increasing data volumes. Oversee the development of data models and schema designs to ensure accurate, accessible, and high-performance data storage for analytics and reporting. Ensure data quality, integrity, and consistency by implementing data validation, monitoring, and error-handling mechanisms. Monitor and troubleshoot performance bottlenecks in data systems, resolving issues with data processing and data access. Stay current with emerging trends and best practices in data engineering, recommending new tools, technologies, and methods to enhance existing infrastructure. Mentor and guide junior data engineers, promoting best practices in data engineering and fostering a collaborative, high-performance team environment.

Requirements

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field (masters degree preferred), and 5+ years of experience in data engineering or software engineering, with demonstrated experience in designing and managing complex data pipelines., or equivalent relevant work experience; e.g., each year of work experience may be substituted for each year of education required.
• Strong understanding of data validation, model testing, and performance evaluation techniques.
• Experience with data visualization and storytelling using tools such as Tableau, Plotly, or Matplotlib.
• Excellent technical communication skills, with the ability to explain complex concepts to non-technical audiences.

Nice To Haves

• 4+ years of experience in applied data science, machine learning engineering, or data pipeline development.
• Proficient in Python, SQL, and distributed data frameworks (e.g., Spark, Databricks, PySpark).
• Experience developing ML models from training to deployment using industry-standard tools and libraries (e.g., scikit-learn, TensorFlow, XGBoost, MLflow).
• Familiarity with MLOps, API development, and secure cloud-based environments (e.g., AWS, Azure, Palantir Foundry).

Responsibilities

• Design, build, and maintain highly scalable, reliable, and efficient data pipelines for extracting, transforming, and loading (ETL/ELT) data from various sources into data warehouses, data lakes, and other storage systems.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and ensure the efficient flow of data across various platforms and systems.
• Develop, deploy, and maintain real-time data streaming solutions to process and deliver data with low latency (using tools like Apache Kafka, Flink, or Spark Streaming).
• Optimize and automate data workflows, ensuring that data pipelines are efficient, reliable, and capable of handling increasing data volumes.
• Oversee the development of data models and schema designs to ensure accurate, accessible, and high-performance data storage for analytics and reporting.
• Ensure data quality, integrity, and consistency by implementing data validation, monitoring, and error-handling mechanisms.
• Monitor and troubleshoot performance bottlenecks in data systems, resolving issues with data processing and data access.
• Stay current with emerging trends and best practices in data engineering, recommending new tools, technologies, and methods to enhance existing infrastructure.
• Mentor and guide junior data engineers, promoting best practices in data engineering and fostering a collaborative, high-performance team environment.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level