Most data teams maintain pipelines. We’re building a data platform that helps an AI system understand how the real world works.
We’ve built a real-world AI model that translates global events into business forecasts. For example, how coffee consumption trends in the United States can predict future sales for garment manufacturers in India. Or how shifts in energy prices, weather patterns, trade flows and commodity markets create ripple effects across industries, often months before they become visible through traditional reporting.
Our mission is to help companies make better decisions by understanding the hidden relationships that shape the global economy.
We’re looking for a Data Engineer to help build the data infrastructure behind that vision.
Role
We’re looking for a Data Engineer who enjoys working with complex, diverse datasets and turning them into reliable systems that others can build on.
You’ll work closely with our data, machine learning and product teams to collect, transform and serve large volumes of economic, industrial, environmental and commercial data.
You will help build and maintain the pipelines, datasets and infrastructure that power our forecasting models and customer-facing products. You should be comfortable taking ownership of well-defined projects, investigating data-quality issues and improving systems as our data platform grows.
This is a hands-on role for someone who has developed strong data-engineering fundamentals and now wants to apply them to a technically ambitious product.
Responsibilities
- Build and maintain reliable data pipelines for structured and unstructured data
- Integrate data from APIs, databases, files and external data providers
- Transform raw data into consistent, documented and usable datasets
- Develop systems for processing large-scale temporal and economic data
- Monitor pipeline performance, data freshness and data quality
- Investigate and resolve data inconsistencies, failures and performance issues
- Work with machine learning engineers to make data available for model training and inference
- Work with product and software engineers to serve data through internal systems, APIs and customer-facing products
- Improve the scalability, reliability and maintainability of the data platform
- Contribute to data architecture, engineering standards and technical documentation
- Help automate manual data-ingestion and validation processes
You Have
- Professional experience in data engineering or a closely related role
- Strong Python and SQL skills
- Experience building and maintaining production data pipelines
- Experience working with relational databases, data warehouses or data lakes
- Familiarity with workflow orchestration and data-transformation tools
- Understanding of data modelling, schema design and software-engineering fundamentals
- Experience working with APIs and external data sources
- Ability to investigate data-quality problems and trace issues across a pipeline
- Comfortable working independently while asking for support when needed
- Strong communication skills and an interest in collaborating across engineering, data science and product teams
- Comfortable operating in a startup environment with changing priorities and incomplete information
Bonus Points
- Experience with time-series, financial, economic, geospatial or industrial data
- Experience with cloud infrastructure such as AWS, Azure or Google Cloud
- Experience with technologies such as Airflow, Dagster, dbt, Spark, Databricks or Snowflake
- Experience building streaming or event-driven data pipelines
- Familiarity with machine learning workflows and feature pipelines
- Experience working with large numbers of external APIs or third-party data providers
- Experience with data observability, lineage or automated quality testing
- Interest in economics, commodities, supply chains, procurement or financial markets
Why Join?
You’ll be working on a problem that sits at the intersection of artificial intelligence, economics and real-world decision-making.
You’ll have the opportunity to build the data foundation of a technically ambitious product, working directly with experienced engineers, machine learning experts and the founding team.
High ownership. High autonomy. Meaningful equity.
A chance to build the infrastructure that helps companies understand what is likely to happen next and what they should do about it.
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