Summary
✨ AI‑Generated
A technology company is seeking an early-career data engineer to build reliable data infrastructure supporting AI-driven products. You will design pipelines from ingestion through transformation and deployment, implement quality checks and anomaly detection, and partner with analytics, data science, and product teams to deliver clean, well-documented datasets.
Highlights
Early-career data engineering role with high ownership and exposure to production AI systems. You will build reliable pipelines, improve data quality, collaborate with analytics and product teams, and work at the intersection of data engineering, AI agents, and product development.
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
