IT Softwaree Engineer

Accountabilities: 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 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. Benefits Base salary range of $115,000–$135,000 , depending on experience. Remote work environment with the opportunity to collaborate across distributed teams. Medical, dental, and vision insurance. Health Savings Account (HSA) and Flexible Spending Account (FSA) options. Generous earned time off. 401(k) and student loan repayment benefits. Life insurance and AD&D insurance. Short- and long-term disability coverage. Employee Assistance Program. Employee stock purchase program. Tuition reimbursement. Performance-based incentive pay. Robust wellness program. Opportunity to work on cloud data infrastructure supporting emerging AI and GenAI applications. Collaborative environment with opportunities for continuous learning and professional development.