Data Engineer

About the role

The Princeton DMIA Integration team is looking for a Data Engineer to own and evolve our data integration practice. You will be responsible for ingesting data from enterprise source systems into our data warehouse platform — what the CIO office refers to as system-to-data-repository integrations. You will play a key role in our migration to a cloud-native data platform, with candidates expected to bring expertise in modern tooling such as Microsoft Fabric, Snowflake with dbt and Fivetran, or Databricks. The existing data warehouse technologies include IBM DataStage, SQL, Oracle, and Shell-based pipelines running on on-premises Linux infrastructure.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines to ingest, transform, and load data from source systems into the enterprise data warehouse.
• Lead the migration of on-premises data pipelines to the organization’s future cloud-native data platform (one of Fabric, Snowflake + dbt + Fivetran, or Databricks).
• Implement and enforce data quality checks, data lineage tracking, and pipeline observability across all integration workflows.
• Ensure data security and compliance requirements are met, including encryption at rest and in transit, and access controls aligned with IAM policies.
• Optimize pipeline performance, scheduling, and resource utilization across batch and incremental load patterns.
• Partner with data analysts, BI developers, and source system owners to understand data requirements and translate them into robust ingestion pipelines.
• Operate and support ingestion and transformation pipelines.
• Develop and maintain data pipeline documentation, data dictionaries, and SLA agreements for ingestion jobs.
• Participate in on-call production support rotation and respond to integration incidents per SLA.
• Contribute to CI/CD pipeline setup and DevOps practices for data integration deployments.

Qualifications

Essential Qualifications

• Linux / Shell scripting
• Python programming
• SQL (query authoring and data validation)
• Basic networking (DNS, HTTP/S, TCP/IP, proxies, firewalls)
• Cloud infrastructure fundamentals (any major provider)
• IAM: LDAP, Active Directory, OAuth 2.0, certificate management
• 5+ years of proven experience with enterprise ETL/ELT tooling
• Advanced SQL skills across multiple platforms (Oracle, SQL Server, PostgreSQL)
• Strong Python programming skills for data transformation, scripting, and pipeline automation
• Shell scripting proficiency for batch job automation on Linux/Unix servers
• Hands-on experience with at least one cloud data platform: Microsoft Fabric, Snowflake (with dbt and/or Fivetran), or Databricks