Data Engineer

Nelnet; Columbus (OH); Ibrido; USD 115.000 - 135.000; Tempo pieno

Vantaggi offerti da questo lavoro

Hybrid work model

Remote-friendly

Descrizione del lavoro

Nelnet is seeking a Data Pipeline Engineer to design, build, and maintain data pipelines for GenAI use cases. You will work with BigQuery, Dataflow, and Airflow to ingest, transform, and serve client data in Google Cloud, ensuring governance and compliance.

The role supports a hybrid work model with remote options and periodic in-office collaboration. The ideal candidate will optimize ELT/ETL workflows, monitor performance and costs, and partner with engineering teams to scale data capabilities

Competenze

• Experience designing, building, and maintaining data pipelines in cloud environments.
• Experience with BigQuery and Dataflow, or Cloud Composer/Apache Airflow.
• Ability to monitor pipeline reliability, performance, and cost; optimize resources.

Mansioni

• Design, build, and maintain data pipelines for GenAI data use cases.
• Develop ELT/ETL processes for batch ingestion from SIS, ERP, and casework systems.
• Collaborate with GCP/Gemini Enterprise Engineer to ensure data readiness and compliance.

Conoscenze

BigQuery

Dataflow

Airflow

ETL/ELT

Dataplex

GCP

Strumenti

BigQuery

Dataflow

Cloud Composer

Apache Airflow

Dataplex

Descrizione del lavoro

Remote

Full time

R23158

Nelnet is a diversified and innovative company committed to enriching lives through the power of service as a student loan servicer, professional services company, consumer loan originator and servicer, payments processor, renewable energy solutions, and K-12 and higher education expert. For over 40 years, Nelnet has been serving its customers, associates, and communities.

The perks of working at Nelnet go beyond our benefits package. When you join the Nelnet team, you're part of a community invested in the success of each individual. That support comes through in our work, as we are united by our mission of creating opportunities for people where they live, learn, and work.

This role owns the data foundation underpinning Nelnet’s GenAI solutions for higher education and SLED clients. The Data Engineer builds and maintains the data pipelines, classification, and governance framework required to make client data safely and reliably usable by agentic AI solutions built on the Gemini Enterprise Agent Platform.

Operating within Nelnet’s GenAI delivery team, this role translates the Forward Deployed Engineer’s scoped requirements into production data pipelines on Google Cloud, working closely with the GCP/Gemini Enterprise Engineer to ensure data is structured, classified, and access-controlled appropriately for AI use. Given the sensitivity of student and institutional data, this role plays a central part in surfacing data‑level compliance risk — such as FERPA — before it reaches production.

Compensation range for this role: $115,000-$135,000 based on experience.

Nelnet believes in a hybrid work environment that accommodates both in‑office and remote work. This model promotes a positive work‑life balance and culture, enabling in‑person collaboration when possible while also providing benefits associated with remote work. The standard hybrid work schedule includes 24 hours of in‑office work each week, for associates that reside within 30 miles of an office. This is subject to change, based on manager discretion.

At this time, we are unable to consider external candidates that reside in these states: Alabama, California, Connecticut, Hawaii, Illinois, Maine, Massachusetts, Michigan, New Jersey, New York, Oregon, Rhode Island, Vermont, Washington.

NOTE: Internal Associates working in a remote capacity may be considered

for this position.

Data Pipeline Engineering

• Design, build, and maintain data pipelines using BigQuery and Dataflow (or Cloud Composer/Apache Airflow) to ingest, transform, and serve client data for GenAI use cases.
• Build and maintain ELT/ETL processes for batch ingestion of client source system data (SIS, ERP, casework systems) into Google Cloud analytics and RAG data stores — distinct from the live, real‑time system access the GCP/Gemini Enterprise Engineer builds for agent tool use.
• Monitor pipeline reliability, performance, and cost, optimizing BigQuery usage and Dataflow jobs as engagements scale.

Data Classification & AI Readiness

• Classify and tag client data using Dataplex and