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# IT Softwaree Engineer - Data

- **Company:** Nelnet
- **Location:** North Platte, NE, United States (Remote available)
- **Salary:** $115,000.0 - $135,000.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, JIRA, BigQuery, C Sharp (Programming Language), Cloud Computing, Cloud Database, Cloud Storage, Databases, Data Architecture, Information Engineering, Data Governance, Data Integrity, Extract Transform Load (ETL), Data Transformation, Data Security, Data Structures, Data Stores, Data Systems, Relational Databases, Database Queries, Document-Oriented Databases, Data Flow Control, Identity and Access Management, Python (Programming Language), PostgreSQL, Team Foundation Server, Operational Databases, Performance Tuning, User Defined Functions, Software Engineering, SQL Stored Procedures, SQL Databases, SQL Server Integration Services, Data Streaming, Technical Data Management Systems, Google Cloud, Cloud Platform System, Data Classification, Real Time Systems, Netezza, Snowflake, Generative AI, Collibra, Amazon Simple Queue Service (SQS), Azure Synapse Analytics, Data Pipelines, Amazon Redshift
- **Published:** September 23, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3401608197&tx=KR3131FFP&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

Bachelor's degree in related field or equivalent work experience, * Hands-on experience building data pipelines and ELT/ETL processes for analytics or GenAI use cases on a major cloud data platform (e.g., BigQuery, Snowflake, Redshift, Synapse) - required. Direct experience with BigQuery and Dataflow (or Cloud Composer/Apache Airflow) strongly preferred; candidates with strong GenAI/AI-adjacent data engineering experience on other platforms who can ramp quickly on Google Cloud will be considered.

 * Experience with data governance and classification tooling for identifying and managing sensitive or regulated data (e.g., Google Cloud Dataplex and Cloud DLP, or equivalent tools such as Collibra, Alation, AWS Macie); direct experience with Google's tools strongly preferred.
 * Working knowledge of cloud storage, messaging, and access-control concepts (e.g., Cloud Storage/S3, Pub/Sub/SNS/SQS, IAM) sufficient to ramp quickly on Google Cloud's specific implementations.
 * Google Cloud certification preferred (Professional Data Engineer), demonstrating hands-on technical fluency; foundational/business-oriented certifications do not satisfy this preference.
 * Strong SQL skills and experience with Python for pipeline development and data transformation.
 * Familiarity with data privacy and compliance considerations relevant to education or public sector data (e.g., FERPA, state privacy law).
 * Experience partnering with client or partner technical teams to extract and integrate data from legacy or third-party systems (SIS, ERP, casework systems).
 * Ability to work from a scoped backlog and translate technical requirements from the Forward Deployed Engineer into working data pipelines., * Proficient in SQL development (complex queries, stored procedures, user defined functions) and performance tuning
 * Strong knowledge of the full software development lifecycle with exposure to agile or iterative approaches to delivery preferred.
 * Proficient in ETL design and development in at least one tool, such as SSIS
 * Basic knowledge of C#.net for SSIS scripting
 * Understanding of RDBMS principles
 * Knowledge of Agile
 * Data base design and modeling experience, preferred
 * JIRA/TFS experience, preferred
 * Netezza, Postgres SQL experience would be helpful
 * Analytical and problem solving skills
 * Ability to maintain high level of confidentiality
 * Must demonstrate a high level of professionalism, positive attitude, and demeanor
 * Accepts responsibility
 * Passionate about delivering working software
 * Ability to adapt and maintain stress tolerance in a rapidly changing environment
 * Ability to work on a distributed team in a virtual environmental and remain motivated and productive
 * Strong verbal and written communication skills
 * Ability to obtain a security clearance

## Description

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.

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 Cloud Data Loss Prevention (DLP) to identify sensitive and regulated data, such as FERPA-protected student records, prior to AI use.
 * Define and enforce data access controls and governance policies appropriate for higher education and SLED data sensitivity.
 * Assess and document data readiness for AI use cases, flagging gaps in quality, completeness, or governance to the Forward Deployed Engineer.

Data Architecture & Integration

 * Design data models and schemas that support both current client use cases and reusable, repeatable data patterns across engagements.
 * Partner with client technical teams to understand source system constraints and negotiate data access and extraction approaches.
 * Maintain technical documentation of data flows, schemas, and classification decisions for internal reuse and audit purposes.

Delivery Execution & Quality

 * Execute against the delivery backlog owned by the Forward Deployed Engineer, providing technical estimates and flagging data-related delivery risks.
 * Partner with the GCP/Gemini Enterprise Engineer to ensure data pipelines feed agentic solutions with the structure and freshness required.
 * Conduct data quality reviews and testing to maintain reliability standards across the practice.

Cross-Functional Collaboration

 * Partner with the GCP/Gemini Enterprise Engineer on data structure and access needed for agentic solutions and retrieval-augmented generation (RAG) pipelines.
 * Partner with the AgentOps Engineer on infrastructure, security, and environment management for data systems.
 * Provide technical input to the Forward Deployed Engineer and Engagement Manager on data-related scope, risk, and timeline., * Demonstrates strong technical judgment in structuring data for both immediate client use and long-term reuse.
 * Identifies data quality, access, and compliance risks proactively before they affect delivery or client trust.
 * Communicates technical data considerations clearly to non-technical stakeholders and the Forward Deployed Engineer.
 * Builds for reuse, translating one-off client data work into repeatable technical assets.
 * Takes ownership of data reliability and quality from ingestion through AI consumption.
 * Collaborates effectively with GCP/Gemini Enterprise Engineering and AgentOps counterparts.
 * Maintains composure and problem-solving focus when data quality or access blockers threaten delivery timelines.
 * Seeks continuous learning given the fast-evolving nature of the Google Cloud data and AI platform.


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