Data Engineer II
Juno Beach, FL (100% on-site)
Duration:
12-month contract (W2, not eligible for C2C)
Pay:$40-45/hr
The Data Engineer is a core member of the HR IT and Corporate Services technology organization, responsible for designing, building, and maintaining the data infrastructure that powers analytics, automation, and AI-enabled capabilities across the enterprise.
This role operates at the intersection of HR and Corporate Services systems and a modern cloud data platform, ensuring that data from operational systems is clean, structured, governed, and reliable for downstream reporting, self-service analytics, and intelligent applications.
Key Responsibilities
Data Pipeline Development & Maintenance
- Design, build, and maintain scalable data pipelines that ingest and transform data from enterprise source systems (e.g., HR, finance, procurement, and service management platforms) into a cloud data platform (e.g., BigQuery, Cloud Storage, Pub/Sub or equivalent)
- Ensure pipelines are reliable, observable, and resilient, with automated monitoring, alerting, and recovery mechanisms
- Support both batch and near-real-time data ingestion patterns
Semantic Layer & Data Modeling
- Define and maintain consistent business definitions for core enterprise entities such as employee, position, organizational unit, cost center, job classification, and related constructs
- Develop and maintain dimensional models and curated data marts for analytics, reporting, and AI consumption
- Resolve inconsistencies in data definitions across multiple source systems
AI & Automation Enablement
- Structure and prepare datasets to support AI use cases, including Retrieval-Augmented Generation (RAG) and LLM grounding
- Collaborate with architects and AI teams to define stable and well-governed data contracts between data pipelines and AI models
- Enable automation and AI-driven workflows by delivering clean, contextual, and well-structured data for enterprise agents and digital assistants
Data Quality & Governance
- Implement data quality rules, validation frameworks, and monitoring across the data ecosystem
- Proactively identify and resolve data integrity issues before they impact reporting or AI outputs
- Support compliance, auditability, and data lineage documentation requirements
- Partner with business data owners to enforce data standards and governance practices
Analytics & Self-Service Enablement
- Build curated datasets and semantic models that enable self-service analytics for business stakeholders
- Translate business requirements into reusable, scalable data products
- Collaborate with analytics and reporting teams to ensure consistent and trusted data access
Platform & Integration Support
- Work with integration teams to understand and manage data contracts across system boundaries
- Contribute to data architecture decisions within broader transformation and modernization initiatives
- Support data migration and system transformation efforts, including updates to pipelines and data models as systems evolve
Required Skills & Experience
Technical
- 3+ years of experience in data engineering, data integration, or related roles
- Strong proficiency in SQL and Python for data transformation and pipeline development
- Experience with cloud data platforms (Google Cloud Platform preferred; Azure or AWS acceptable)
- Familiarity with data pipeline and transformation tools (e.g., Dataflow, Apache Beam, dbt, or similar)
- Understanding of APIs and data formats such as REST, JSON, XML, and flat files
- Knowledge of data warehousing concepts, dimensional modeling, and semantic layer design
Domain
- Experience working with enterprise systems (e.g., HR, finance, procurement, or service management platforms) preferred
- Ability to translate business requirements into technical data solutions
Mindset
- Treats data as a product, focusing on usability, reliability, and long-term maintainability
- Comfortable working with complex and inconsistent source systems
- Proactive approach to identifying and resolving data quality issues
Preferred Skills
- Experience with integration platforms or middleware (e.g., SAP CPI or equivalent)
- Familiarity with dbt for transformation and semantic modeling
- Exposure to AI/ML data preparation, including RAG and LLM grounding concepts
- Experience in regulated environments where auditability and compliance are important
- Cloud data engineering certification (or working toward one) such as Google Cloud Professional Data Engineer
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