Data Engineer

This is a hybrid role and requires 3 days onsite in the Buffalo, NY area. Our client will strongly consider data engineering generalists and enterprise ETL, integrationj, data warehouse specialists, including those coming from platforms like Informatica, Boomi, SSIS, Talend, or MuleSoft, who are willing to develop hands-on expertise in Oracle Fusion Data Intelligence (OFDI). Oracle experience is a plus, not a prerequisite: the ability to independently design a multi-system data architecture matters more than mastery of any single platform.

WHAT YOU'LL DO

• Serve as the primary technical owner of Oracle Fusion Data Intelligence (OFDI), standing up, configuring, and evolving the platform as the enterprise analytics hub.
• Independently design the enterprise data architecture, including data modeling, repository structure, and segmentation strategy, across disparate source systems (SAP, on-prem Oracle, SQL Server, GIS, and others).
• Design and build scalable ETL/ELT pipelines that ingest data from multiple source systems into a centralized data warehouse.
• Develop and maintain integrations using Oracle Integration Cloud (OIC) as the enterprise middleware platform.
• Transition the organization away from legacy flat file integrations toward API-based, event-driven data flows.
• Implement data quality checks, reconciliation processes, and monitoring controls to support auditability and regulatory compliance.

• Curate trusted, analytics-ready datasets for consumption by downstream BI tools including Power BI and Oracle Analytics Cloud.
• Build and maintain semantic data models and data mappings that support self-service reporting and enterprise analytics initiatives.
• Collaborate with application and business teams to define data requirements across operational, financial, regulatory, and customer domains.

• Establish data governance frameworks, data lineage documentation, and data quality standards from the ground up.
• Document data flows, technical designs, and integration specifications to ensure long-term maintainability.
• Partner with enterprise architecture and application teams to align data solutions with enterprise standards.
• Evaluate and recommend supplemental data tools, including platforms such as Microsoft Fabric, as platform needs evolve, with significant input into tooling decisions.

• Operate with a high degree of autonomy in a self-directed capacity; minimal technical oversight exists above this role, and there is currently no internal mentor for this specific skill set.
• Identify gaps in the team's future capability needs and contribute to hiring decisions as the data engineering function grows. NPAworldwide