Data Engineer

About the role

Terradot is seeking an early-career Data Engineer to help build and operate the data infrastructure that supports our scientific and operational work.

You will focus on moving data reliably from field, laboratory, operational, and external sources into well-structured, accessible data systems. Your primary responsibilities will include data ingestion, pipeline development, workflow orchestration, warehouse modeling, testing, and monitoring.

This role is well suited to an engineer in their second or third professional role who has developed a solid software engineering foundation and is ready to take greater ownership of production data systems. You do not need to arrive as an expert in climate, geospatial data, or Enhanced Rock Weathering. You should be excited to learn the domain and apply strong engineering practices to complex real-world data.

You will work closely with data scientists, software engineers, scientists, and operational teams to ensure that trusted data is available for analysis, modeling, measurement, and decision-making.

What you'll do

• Build and maintain data pipelines that ingest information from field operations, laboratories, internal applications, vendors, APIs, and public datasets.
• Develop and operate orchestrated workflows for recurring ingestion, transformation, validation, and delivery processes.
• Design clear, maintainable data models within Terradot’s data warehouse.
• Improve the reliability, performance, and usability of data used by scientific and operational teams.
• Implement automated testing, monitoring, alerting, and documentation for production data workflows.
• Investigate pipeline failures, data quality issues, and inconsistencies across source systems.
• Build reusable integrations and tools that reduce manual data movement and improve self-service access.
• Collaborate with data scientists to productionize the datasets and transformations required for geospatial and statistical analysis.
• Contribute to shared engineering standards through code review, testing, documentation, and thoughtful system design.
• Help improve how Terradot manages data lineage, permissions, schemas, and ownership as our systems scale.

What we are looking for

• Professional experience building or maintaining production software or data systems, typically gained through a second or third engineering role.
• Strong programming skills in Python and working proficiency in SQL.
• Experience building data pipelines, backend services, API integrations, or automated data workflows.
• Familiarity with at least one workflow orchestration system, such as Airflow, Dagster, or Prefect.
• Experience working with relational databases and data warehouses.
• Understanding of software engineering fundamentals, including version control, testing, code review, debugging, and maintainable system design.
• Ability to investigate problems across the full data lifecycle, from source systems through transformed datasets.
• Clear communication skills and an interest in working with colleagues from scientific, operational, and commercial backgrounds.
• Fluent English communication skills
• Comfort working in an environment where requirements may evolve as the science and operations develop.

We value demonstrated ability and growth potential more than experience with a particular technology stack.

Nice to Have

• Experience with dbt or similar data transformation frameworks.
• Familiarity with cloud platforms such as AWS or GCP.
• Experience with containerization, infrastructure as code, or continuous integration and deployment.
• Exposure to geospatial data or tools such as PostGIS, GDAL, GeoPandas, rasterio, Zarr, or STAC.
• Experience working with sensor, laboratory, agricultural, environmental, or operational data.
• Familiarity with data observability, metadata management, or lineage systems.

Personal Attributes

• Practical and action-oriented, with a focus on building systems that work reliably.
• Curious about unfamiliar scientific and operational domains.
• Comfortable asking questions and making ambiguity more concrete.
• Thoughtful about balancing immediate delivery with maintainable engineering.
• Collaborative and receptive to feedback.
• Motivated by measurable improvements in reliability, accessibility, and team effectiveness.