Data Engineer at ECPI University in Virginia Beach, VA

Job description

• This is not a remote position. This position is based at our Virginia Beach, VA location.
• Good Faith Pay Range: In accordance with Virginia law, the good-faith pay range for this position is $105,000 - $128,000 per year. Actual compensation will be determined based on the candidate's qualifications, education, experience, skills, and other legitimate business factors.
• The Data Engineer designs, builds, and owns the pipelines and modeled data sets that make up ECPI University's Snowflake data platform. Working directly with the Senior Director of Solution Architecture, this position takes significant components end to end: source system integration, transformation logic, data modeling, performance, cost, and reliability.
• This is a hands-on engineering role, not an analyst or reporting position. The University is consolidating data from a range of enterprise and student-facing systems onto a single cloud data platform, and this position is central to whether the information faculty, academic leadership, and student support staff use is accurate, timely, and trusted.

Responsibilities

• Data Platform Engineering
• Design, build, and own end-to-end pipelines that ingest data from enterprise SaaS applications, student systems, and operational databases into Snowflake, selecting the appropriate pattern for each source across batch, API extraction, change data capture, and near real time streaming.
• Develop transformation logic in SQL and Python as version-controlled, tested, documented code, with orchestration for scheduled and event-driven workloads.
• Contribute to platform architecture with the Senior Director of Solution Architecture, including layering strategy, standards, and reusable patterns.
• Data Modeling and Delivery
• Design dimensional, analytics-ready data models that serve reporting, analytics, and downstream integrations, translating requirements from academic, enrollment, financial aid, student services, and administrative stakeholders into durable models rather than one-off extracts.
• Establish certified data sets that serve as the authoritative source for key institutional measures, and retire the redundant reports and manual extracts they replace.
• Reliability, Performance, and Security
• Own operational reliability for assigned pipelines, including monitoring, alerting, incident response, and root cause analysis, and implement automated data quality testing covering freshness, completeness, uniqueness, and business rules.
• Tune Snowflake for both performance and cost, including warehouse sizing, clustering, query optimization, and resource monitors, and implement role-based access control, masking, and least-privilege access consistent with FERPA, GLBA, and University policy.
• CI/CD and AI-Assisted Delivery
• Own CI/CD for data platform code: automated build, test, and deployment through GitHub Actions or comparable, with environment promotion, rollback, and automated tests as a deployment gate.
• Manage data platform objects as code so that environments are reproducible and changes reviewable, and replace manual data movement, reconciliation, and hand-run reports with automation.
• Set the standard for AI-assisted engineering practice: effective prompting, rigorous review of generated code, and clear accountability that the engineer owns the output. Contribute to University governance for AI-assisted development.
• Design and implement solutions using the platform's native AI capabilities, including Snowflake Cortex functions, embeddings, and vector search, and advise on when a native capability is the right choice compared with an external service.
• Technical Leadership
• Mentor the Associate Data Engineer and other colleagues through code review, pairing, and direct instruction, and define the standards and reference implementations other engineers apply.
• Communicate technical tradeoffs clearly to technical and non-technical audiences, including IT leadership, and perform other duties as assigned.

Requirements

• Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field, or an equivalent combination of education and experience, plus four or more years of professional data engineering experience, including two or more years hands-on in Snowflake.
• Production Snowflake depth across virtual warehouses, micro-partitioning, clustering, streams and tasks, Snowpipe, and time travel, plus roles, grants, and masking policies, and demonstrated ability to diagnose and resolve performance and cost issues.
• Advanced SQL, including window functions, common table expressions, and incremental and merge patterns, proficiency in Python for data engineering, and working knowledge of dimensional modeling.
• Experience with dbt or a comparable modular, tested approach to SQL transformation, and with orchestration tooling such as Airflow, Dagster, Azure Data Factory, or Snowflake tasks.
• Experience with ETL/ELT data integration tooling for source ingestion and flow orchestration, such as Snowflake Openflow (Apache NiFi), Snaplogic, Fivetran, Matillion, or a comparable dataflow/integration platform.
• Demonstrated CI/CD experience for data or software delivery through GitHub Actions, Azure DevOps, or a comparable tool, with automated data testing enforced in the pipeline.
• Daily, practical use of AI coding assistants in production engineering work with a disciplined validation habit. Candidates should be able to describe specifically where these tools accelerate their work and where they mislead.
• Ability to resolve ambiguity independently and communicate clearly with functional stakeholders in an Agile (Scrum) environment.
• SnowPro Advanced Data Engineer or SnowPro Core certification, or hands-on experience with Snowflake Cortex, embeddings, or vector search.
• Experience in Azure or AWS, including storage, identity, and secrets management, or infrastructure as code such as Terraform.
• Higher education experience, particularly student information systems, learning management systems, or institutional reporting, or integration of a major HCM, ERP, or enrollment CRM platform.
• Experience establishing AI-assisted development standards for a team, or mentoring junior engineers.

Skills

Required

• Python for data engineering
• Dimensional modeling
• Agile (Scrum) environment

Preferred

• Infrastructure as code (Terraform)
• Mentoring junior engineers

Benefits

• Placeholder range: $105,000 to $128,000 annually, with a midpoint near $115,000.
• Tuition scholarship program available to full-time employees and their immediate family members after 90 days of employment
• Competitive compensation and medical and dental benefit plans
• PTO and holiday pay
• 401(k) participation with possible employer contributions