Data Engineer

Location: Raleigh, NC (onsite, 3 days/week)

Data Engineer

We're looking for a Data Engineer to develop and enhance application components that support ML/AI models and data ingestion pipelines. You'll lead and collaborate with a team of developers, work closely with business stakeholders, and ensure that data systems and statistical models are production-ready, resilient, and scalable.

How to apply

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What You'll Do

Data Engineering & Pipeline Development

• Design, develop, and maintain ETL pipelines and data ingestion processes supporting ML/AI models
• Build and enhance Hive and DBMS-based applications for large-scale data processing
• Develop PySpark and Spark jobs for big data transformation and analytics workloads
• Write and optimize Oracle SQL/PLSQL stored procedures and queries on Exadata

ML/AI Model Support

• Develop components that prepare and serve data for machine learning model training and inference
• Implement and deploy statistical models using Python libraries (scikit-learn, scipy, numpy, pandas)
• Work in Jupyter notebooks to prototype, evaluate, and document model pipelines
• Ensure models are production-ready with a focus on code resiliency and stability

Collaboration & Leadership

• Lead and mentor a team of developers on data engineering best practices
• Partner with business stakeholders to translate requirements into scalable data solutions
• Participate in design and code peer reviews
• Contribute to analysis of operational issues and drive resolution
• Work in a fast-paced Agile environment with minimal supervision

What We're Looking For (Required

• Strong knowledge of Oracle, SQL, and RDBMS systems including Oracle Exadata
• Hands-on experience with Hadoop, Hive, Spark, and PySpark for big data workloads
• Python programming proficiency including scripting and object-oriented design
• Experience with ETL development and data pipeline architecture
• Familiarity with statistical modeling libraries: Jupyter, scipy, numpy, pandas, scikit-learn
• Working knowledge of machine learning concepts and model lifecycle

Nice to Have

• Experience automating and deploying ML models in a production environment
• Knowledge of Autosys for job scheduling
• Prior experience with Horizon tools: Jira, Bitbucket
• Familiarity with Natural Language Processing (NLP) techniques including semantic search, classification, and information extraction
• Demonstrated ability to manage senior stakeholder expectations and build trust