Data Intern

We're looking for a Geospatial Data Engineering Intern to help build and scale our geospatial data infrastructure over the summer. This role is designed for a junior or senior undergraduate with at least one prior internship under their belt, strong data engineering instincts, and the team awareness to ship work in a shared production codebase. You'll get hands-on experience building pipelines that ingest, transform, and serve geospatial data with exposure to AI agent tooling along the way. This role begins as a full-time, 3-month summer internship and then continues part-time through September.

You'll work directly with our data team, contributing to operational infrastructure that powers geospatial analysis and decision-making across the organization. Your primary focus will be building reliable, well-documented data pipelines with a geospatial backbone, while getting meaningful exposure to applied AI systems and helping us complete an in-flight migration from Airflow 2 to Airflow 3.

About your role at Ready ⚡️

You’ll spend the majority of your time on geospatial data engineering, with supporting work in geospatial analysis and applied AI.

Geospatial Data Engineering (Primary Focus)

• Build and improve Airflow ELT pipelines that ingest, transform, and serve geospatial datasets at scale, working across both our Airflow 2 and Airflow 3 repositories and actively assisting with the Airflow 2 to 3 migration, including porting DAGs, validating parity, and helping retire legacy pipelines

• Write clean, type-hinted Python and well-structured SQL, including geospatial operations (PostGIS, spatial joins, CRS management) against Athena (Trino), PostgreSQL, Redshift and duckdb

• Develop modular dbt models with semantic layer definitions and documented business logic for geospatial tables

• Contribute to data quality systems, including schema validation, freshness monitoring, and spatial integrity checks

• Support DataHub adoption through schema documentation, lineage tracking, and metadata management for geospatial assets

• Triage failing DAG runs, read Airflow task logs, and own fixes end-to-end

• Communicate progress through documentation, code reviews, and regular updates

GeoData Science (Supporting Research)

• Contribute to research-oriented analyses such as tree canopy classification, network resiliency analysis, and spatial feature extraction

• Design and document reproducible analytical workflows that feed into production pipelines

• Translate complex geospatial methods into clear, accessible outputs for non-technical stakeholders

• Share learnings on emerging GeoAI methods and geospatial tooling with the team

AI Engineering (Applied Exposure)

• Assist with building data agents using tools like LangGraph, LangChain, or Bedrock Agent Core

• Support development and iteration on pipelines and text-to-SQL approaches for natural-language data access

• Contribute to MCP server development and agent evaluation as needed

• Document agent failure modes and help refine prompts based on feedback

A bit about you 🥇

• Currently a junior or senior undergraduate (or higher) in Computer Science, Data Science, GIS, Geospatial Engineering, Software Engineering, or a related field

• At least one prior internship (or equivalent team-based engineering experience); you've shipped code in a shared repo, taken a code review, and worked a ticket end-to-end

• Available to work full-time for 3 months during the summer, then part-time through the fall semester

• Strong fundamentals in Python, including classes, inheritance, decorators, type hints, and explicit imports

• Strong fundamentals in SQL: joins, CTEs, window functions, and aggregations

• Comfortable working in Git/GitHub with a dev → main PR-to-deploy workflow

• Comfortable on the Unix command line or eager to learn (bash, navigating a filesystem, running scripts)

• Familiarity with geospatial concepts (CRS, spatial joins, indexing) and tooling such as PostGIS, GeoPandas, or QGIS is a plus

• Exposure to AWS or similar cloud providers; ideally S3, IAM, Athena, Glue, ECS, or Redshift

• Experience with Airflow or similar orchestration tools is a plus (or strong eagerness to learn quickly. You'll be ramping on two versions in parallel and contributing directly to our migration effort)

• Familiarity with dbt, Pandas, or Parquet/columnar data is a plus

• Exposure to AI agent architectures (e.g., ReAct) and protocols (A2A, MCP, AG-UI) is a