Data engineer

FRI is building out its data infrastructure to support a growing portfolio of research projects. We have dozens of active projects, each generating forecasting data from surveys, expert panels, and AI systems. We are looking for a Data Engineer to own and develop FRI’s data platform—from ingesting raw survey data to delivering clean, reliable datasets that power our research.

You would be FRI’s first dedicated data engineer, working initially alongside an external vendor to onboard and extend an existing data warehouse, and eventually taking full ownership of our data infrastructure. This is a foundational role with significant autonomy: you will make key architectural decisions, choose tooling, and shape how FRI manages, transforms, and serves data across the organization.

CORE RESPONSIBILITIES INCLUDE

• Managing data quality, access controls, and documentation so that a team of about 10 analysts can reliably query and use the data
• Supporting infrastructure for LLM-based research (e.g., serving data for AI forecasting benchmarks like ForecastBench)
• Building and maintaining data pipelines that ingest data from multiple sources (survey platforms, metadata stores, external datasets) into a cloud data warehouse
• Designing and evolving a dimensional data model (staging, dimension, fact, and mart layers) that standardizes forecasting data across FRI’s projects
• Setting up orchestration, monitoring, and alerting for production data pipelines

QUALIFICATIONS

Successful candidates will have the following characteristics:

• Passionate about FRI’s mission of improving decision-making on high-stakes issues
• Strong Python skills and experience building data pipelines (ETL/ELT)
• Proficient in SQL and dimensional data modeling (star schema, staging/dimension/fact layers)
• Experience with cloud data warehouses (e.g., BigQuery, Snowflake, Redshift)
• Clear, explicit communicator, both in writing and verbally
• Ability to work productively in a self-directed (remote) environment

The ideal candidate would have the following characteristics:

• Familiarity with modern data stack tools (e.g., dbt, Airflow, Dagster) or similar transformation and orchestration frameworks
• Experience with version control, CI/CD, and infrastructure-as-code practices (e.g., Git, Docker, Terraform)
• Prior software engineering experience (e.g., backend development, web development, or maintaining production systems)
• Comfortable deploying and operating services on a major cloud platform (e.g., GCP, AWS, Azure)
• Interest in quantitative forecasting (including ML/AI)

We highly encourage you to apply, even if you don’t feel completely qualified. Some of the best people we’ve worked with in our careers felt underqualified when they first applied, and we’re glad they did so anyway.