Data Engineer II

Data Engineer II Contract Type:

Contractor Location:

Juno Beach Industry:

Utilities Contact Name:

Katie Jreij Contact Phone:

Date Published:

13-Apr-2026 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 ...