Data is the lifeblood of Global Predictions. We use data from extremely diverse areas like politics, public health, macroeconomics, financial markets, demographics, and more. Your role will be to find, import, clean, transform, and extract insights from that data so that the rest of the organization can thrive. This role will carry a lot of ownership and responsibility, and will continue to grow in importance as the company scales.
- Create and maintain optimal data pipeline & architecture
- Assemble large, quality data sets that meet socio-economic modeling requirements by building out and maintaining APIs and web scrapers
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using MySQL and AWS ‘big data’ technologies.
- Build access and analytics tools that utilize the data pipeline to provide actionable insights for the Quantitative Economics, Software, and ML/AI teams
- Construct automated quality checks across the data store to ensure quality control
- Collaborate to build ML tools to increase automated insight generation and integration to core GP Knowledge Graph and macroeconomic models
- Manage 3rd party data vendor and QA contractor relationships
- 5+ years of experience in a Data Engineer or similar role
- Minimum undergraduate degree in Software Engineering or Computer Science
- Professional experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases
- Experience with most of the following tools: SQL, NoSQL, Postgres, Hadoop, Spark, Kafka, Azkaban, Airflow, AWS EC2/EMR/RDS/Aurora, and Storm
- Experience building and optimizing ‘big data’ data pipelines, architectures and data sets
- Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement
- Strong analytic skills related to working with unstructured datasets
- Build processes supporting data transformation, data structures, metadata, dependency and workload management
- Basic understanding of ML preprocessing, training, and testing pipelines