Job Title: - Data Engineer
Job Location: Juno Beach, FL
Job Type: Long-term Growth
Job Description:
Data Engineer - HRIT & Corporate Services IT
Information Technology Draft - v0.1
About the Role
The Data Engineer is a foundational role within the HR IT and Corporate Services IT organization, responsible for building and sustaining the data infrastructure that powers analytics, automation, and AI-driven capabilities across the enterprise. This role sits at the intersection of HR/Corporate Services systems and the Google Cloud Platform data stack, ensuring that data flowing from operational platforms is clean, structured, governed, and ready to support intelligent applications.
As advances its AI activation agenda - including Gemini Enterprise, Vertex AI, and the HR Services 2027 initiative - the Data Engineer ensures the semantic layer above core systems remains coherent and trustworthy. Without this foundation, automation becomes brittle and AI outputs become unreliable.
Key Responsibilities
Data Pipeline Development & Maintenance
Design, build, and maintain data pipelines that move and transform data from source systems (SAP SuccessFactors, ServiceNow HRSD, SAP S/4HANA, Fieldglass, SAP IAS) into Google Cloud Platform (BigQuery, Cloud Storage, Pub/Sub)
Ensure pipelines are reliable, observable, and recoverable - with automated alerting for failures or data anomalies
Manage data ingestion patterns for both batch and near-real-time use cases
Semantic Layer & Data Modeling
Define and maintain consistent semantic definitions for core HR and Corporate Services entities: employee, position, organizational unit, cost center, pay grade, job classification, and related constructs
Build and govern dimensional models and data marts that serve reporting, self-service analytics, and AI grounding use cases
Resolve definitional conflicts across systems (e.g., where SuccessFactors and SAP S/4 represent the same concept differently)
AI & Automation Enablement
Prepare, label, and structure datasets to support Retrieval-Augmented Generation (RAG) patterns and LLM grounding for Gemini and Vertex AI applications
Partner with solution architects and AI practitioners to ensure data contracts between pipelines and AI models are well-defined and stable
Support agent and automation use cases (ServiceNow, Now Assist, Google Agentspace) with clean, structured context data
Data Quality & Governance
Implement data quality rules, validation checks, and monitoring across the HRIT data estate
Identify and escalate data integrity issues before they surface in dashboards, reports, or AI outputs
Support audit and compliance requirements by maintaining data lineage documentation and access controls
Collaborate with HR and Corporate Services data owners to establish and enforce data standards
Analytics & Self-Service Enablement
Build and maintain curated datasets and semantic models in BigQuery / Looker that enable HR leaders, Corporate Services leaders, and HRIT team members to access trusted data without custom IT requests
Partner with reporting and analytics consumers to understand requirements and translate them into reusable data products
Platform & Integration Support
Collaborate with SAP CPI and integration teams to understand data contracts and transformation logic at system boundaries
Contribute to data architecture decisions as part of the broader REWIRE and Google stack activation program
Support data migration efforts (e.g., S/4 transformation, SuccessFactors module expansions) with pipeline and model changes
Required Skills & Experience
Technical
3+ years of experience in data engineering, data integration, or a closely related role
Proficiency in SQL and Python for data transformation and pipeline development
Experience with cloud data platforms - Google Cloud Platform (BigQuery) preferred; Azure or AWS considered
Familiarity with data pipeline frameworks (e.g., Apache Beam, dbt, Dataflow, or equivalent)
Working knowledge of REST APIs and data exchange patterns (JSON, XML, flat file)
Understanding of dimensional modeling, data warehousing concepts, and semantic layer design
Domain
Exposure to HR or enterprise business systems (SAP SuccessFactors, ServiceNow, SAP S/4HANA, or similar) strongly preferred
Ability to work with business stakeholders to translate data needs into technical requirements
Mindset
Treats data as a product - thinks about consumers, reliability, and usability, not just pipeline execution
Comfortable operating in an environment where source systems are complex and definitions are inconsistent
Proactive about data quality - finds breaks before users do
Preferred Skills
Experience with SAP CPI or other middleware/integration platforms
Familiarity with dbt for data transformation and semantic modeling
Exposure to LLM grounding, RAG patterns, or AI/ML data preparation
Experience supporting regulated industries (energy, finance, healthcare) where auditability matters
Google Cloud Professional Data Engineer certification (or in progress)
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