IT Softwaree Engineer

United StatesRemoteFull-timemid

OtherEngineer

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an IT Software Engineer - Data based in the United States.

Technical Tools

OtherEngineer

This remote role owns the data foundation supporting advanced GenAI solutions for higher education and public-sector clients.
You will design, build, and maintain reliable cloud data pipelines that make sensitive client information usable for AI applications.
The role combines data engineering, governance, classification, security, and architecture in a highly collaborative environment.
You’ll work primarily with Google Cloud technologies, supporting analytics, retrieval-augmented generation (RAG), and agentic AI use cases.
A key part of the role is identifying data quality, access, privacy, and compliance risks before they affect production systems.
You’ll partner with engineering, delivery, operations, and client technical teams to turn scoped requirements into scalable solutions.
The position offers an opportunity to contribute to reusable data and AI capabilities while working in a distributed, fast-evolving technology environment.

Design, develop, and maintain cloud data pipelines using technologies such as BigQuery, Dataflow, Cloud Composer, or Apache Airflow to ingest, transform, and serve data for GenAI use cases.
Build and maintain batch ETL/ELT processes that integrate data from source systems such as student information systems, ERP platforms, and casework applications into cloud analytics and RAG data stores.
Monitor pipeline reliability, performance, scalability, and cost, optimizing data processing workflows as client engagements grow.
Classify and tag sensitive data using data governance and loss-prevention tools, identifying regulated information and establishing appropriate controls before AI consumption.
Define and enforce data access, governance, and security policies appropriate for sensitive education and public-sector information.
Assess data quality, completeness, readiness, and governance for AI use cases, proactively identifying gaps and delivery risks.
Design data models and schemas that support immediate client requirements while creating reusable patterns for future engagements.
Collaborate with client technical teams to understand source-system constraints and determine effective data extraction and integration approaches.
Maintain clear technical documentation covering data flows, schemas, classification decisions, and integration approaches for reuse and auditability.
Execute against a defined delivery backlog, provide technical estimates, and communicate data-related risks, dependencies, and timeline impacts.
Partner with cloud, AI, infrastructure, security, and delivery teams to ensure data pipelines provide the structure, access, and freshness required by AI applications.
Conduct data quality testing and reviews while taking ownership of reliability from ingestion through AI consumption.

Requirements

~2 min read

Bachelor’s degree in a related field or equivalent professional experience.
Hands-on experience building data pipelines and ETL/ELT processes for analytics, AI, or GenAI use cases on a major cloud data platform such as Google Cloud, Snowflake, AWS, or Azure.
Direct experience with BigQuery and Dataflow, Cloud Composer, or Apache Airflow is strongly preferred, with transferable cloud data engineering experience also relevant.
Experience with data governance, classification, and sensitive-data management tools such as Dataplex, Cloud DLP, Collibra, Alation, or AWS Macie.
Working knowledge of cloud storage, messaging, identity, and access-control concepts, including technologies such as Cloud Storage/S3, Pub/Sub, SNS/SQS, and IAM.
Strong SQL skills, including complex queries, stored procedures, user-defined functions, and performance tuning.
Experience using Python for data pipeline development and transformation.
Knowledge of ETL design and development, relational database concepts, database modeling, and data architecture.
Familiarity with Agile or iterative software development practices and the broader software development lifecycle.
Experience integrating data from legacy or third-party systems, including enterprise, education, or case-management platforms.
Familiarity with data privacy and compliance considerations relevant to education or public-sector information, including FERPA and applicable privacy regulations.
Google Cloud Professional Data Engineer certification is preferred.
Experience with tools or technologies such as SSIS, C#/.NET scripting, PostgreSQL, Netezza, Jira, or TFS is beneficial.
Strong analytical, problem-solving, organizational, and technical judgment skills.
Ability to identify data quality, access, security, and compliance risks proactively.
Strong written and verbal communication skills, with the ability to explain technical concepts to both technical and non-technical stakeholders.
Ability to work independently from a scoped backlog while collaborating effectively with distributed, cross-functional teams.
High level of professionalism, confidentiality, ownership, adaptability, and resilience in a rapidly changing environment.
Ability to obtain a security clearance.