Data Engineer Intern - Cloud Data Platform, ML & AI

Farm Credit Bank of Texas•Austin, TX

1d•Onsite

About The Position

The Data Engineer Intern supports the enterprise Cloud Data Platform team, working under the guidance of the Director, Data Architecture & AI Toolkit. This role is designed for a Master’s‑level Computer Science student seeking hands‑on experience building, enhancing, and innovating small tools, utilities, and platform components across data engineering, analytics enablement, and ML/AI workflows. The intern will contribute to production‑adjacent engineering work, focusing on automation, platform utilities, data pipelines, quality checks, and AI‑assisted tooling that improve developer productivity, data reliability, and platform efficiency.

Requirements

Strong foundation in Python and SQL
Familiarity with data engineering concepts such as ETL/ELT, data modeling, and batch & stream processing
Exposure to cloud platforms (Databricks, Azure, AWS, or GCP) and modern data tools
Experience using Git and collaborative development workflows
Strong analytical and problem-solving skills
Curiosity and willingness to learn complex data and platform concepts
Ability to work independently on well-defined tasks while collaborating with senior engineers
Ability to collaborate and excel in complex, cross-functional teams involving data engineers, business analysts, and stakeholders
Clear written and verbal communication skills
Attention to detail and quality-focused mindset
Currently pursuing a Master’s degree in Computer Science, Data Science, Software Engineering, or related field OR recently completed a Master’s degree in Computer Science or a related field and eligible to work in the U.S. under Optional Practical Training (OPT)

Nice To Haves

Detailed understanding of ML concepts such as feature engineering, training pipelines, or model inputs is a plus

Responsibilities

Assist in the design and development of data pipelines, ingestion utilities, and transformation logic on cloud data platforms (Databricks, Azure, etc)
Build small internal tools and utilities to support platform operations, developer experience, and governance automation
Assist in implementing data quality scorecard framework covering data validation, reconciliation, and quality checks
Experiment with AI‑assisted tooling, such as metadata enrichment, data quality recommendations, or intelligent monitoring prototypes
Collaborate with platform and analytics teams on proof‑of‑concepts involving ML or GenAI capabilities
Support implementation of bronze / silver / gold style data processing patterns under senior engineer guidance
Develop reusable scripts, libraries, and utilities to simplify common platform tasks
Assist with automation for monitoring, validation, data quality checks, and operational reporting
Contribute to CI/CD pipelines, configuration scripts, and deployment automation where applicable
Support development of data preparation and feature engineering utilities for ML and AI use cases
Gain exposure to data platform operations, including incident analysis, performance tuning, and cost optimization activities
Help document testing approaches and quality metrics
Create and maintain technical documentation, diagrams, and runbooks for tools and utilities developed
Present work outcomes and learnings to the platform engineering team