Summary
✨ AI‑Generated
Join an AI-focused technology team as an early-career data engineer and help build the data foundation behind production applications. You will design reliable pipelines, manage ETL/ELT from ingestion through deployment, implement data-quality controls, and deliver well-documented datasets in partnership with analytics, data science, and product teams.
Highlights
Early-career opportunity with high ownership and direct impact on production AI systems. You will build reliable data infrastructure, own ETL/ELT workflows end-to-end, improve data quality, and collaborate closely with analytics, data science, and product teams.
Description
Jobright is your personal AI job search agent transforming the job search process.
They are seeking an early-career Data Engineer to design and build data pipelines, ensuring data quality and collaborating with analytics and product teams.
Why Join Us
• Build real, production AI agents used by real users
• High ownership and impact
• Work at the intersection of AI, agents, and product
• Shape how people experience AI-driven job search
Responsibilities
• Design and build reliable data pipelines that move data from diverse sources into centralized warehouses and lakes for downstream consumption
• Own the full lifecycle of ETL/ELT processes, from schema design and ingestion to transformation, testing, and deployment
• Ensure data quality and consistency by implementing validation checks, anomaly detection, and automated alerting across critical pipelines
• Partner with analytics, data science, and product teams to understand their data needs and deliver clean, well-documented datasets
• Optimize query performance, storage costs, and pipeline reliability as data volumes and complexity grow
Qualification
Required
• Recent graduate or early-career professional (0–2 years of experience) with a degree in Computer Science, Data Engineering, or a related technical field
• Strong proficiency in Python and SQL, with hands-on experience writing and debugging data transformation logic
• Practical understanding of data modeling, warehouse design patterns, and the trade-offs between batch and streaming architectures
• Solid software engineering foundations, including comfort with APIs, version control (Git), and writing testable, production-ready code
Preferred
• Previous internship or project experience building end-to-end data pipelines or managing datasets at non-trivial scale
• Familiarity with cloud data platforms such as Snowflake, BigQuery, Redshift, or Databricks
• Hands-on exposure to orchestration tools like Airflow, dbt, or Prefect, and streaming technologies like Kafka or Spark Streaming
• Curiosity about how data powers product decisions, and a willingness to dig into messy, ambiguous data problems
