Liam McElhaney

Data engineer and scientist

Data integration, data warehousing, ETL (Extract, Transform, Load) processes, database design, SQL/database management, distributed computing frameworks (e.g., Hadoop, Spark), cloud platforms (e.g., AWS, Azure, GCP), data pipeline development, data architecture, data quality assurance, data governance, scalability and performance optimization, version control systems, software development (Python, Java, etc.), containerization (Docker), orchestration tools (Kubernetes), CI/CD pipelines, API development, data security Statistical analysis, machine learning algorithms, data modeling, data visualization, Python/R programming, SQL/database querying, data preprocessing, experimental design, predictive modeling, natural language processing, deep learning, hypothesis testing, feature engineering, data storytelling, domain knowledge (e.g., American politics, marketing, political psychology, polling), project management.