FocusKPI is looking for a Data Engineer to join or internal team and help one of our clients with their projects.
Our internal team is looking for a non-traditional Data Engineer (DE) —one that blends Data Engineering (DE) and Data Science (DS). This person needs to develop and optimize data architectures that support business intelligence, predictive analytics, and AI/ML applications. This role involves designing and structuring data pipelines that connect SAP, Databricks, and Microsoft CRM, enabling trend analysis, forecasting models, and AI-driven insights. The focus is not just on ETL pipelines but on building a feature store rather than traditional data models.
This role requires a deep understanding of how data should be organized to support efficient reporting, historical trend analysis, and advanced analytics.
Given our global operations, fluency in both English and Chinese is highly beneficialWork Location: Remote - anywhere in the US
Duration: 9+ months contract
Pay Range: $60/hr to $85/hr (on basis of experience level)

Responsibilities:

• Develop and implement scalable data models that link SAP, Databricks, and Microsoft CRM, ensuring they support business intelligence, forecasting, and AI-driven analytics.
• Build robust data pipelines to extract, process, and structure information from SAP HANA, SAP BW, OData, and Microsoft CRM, ensuring accuracy and usability for analytical tools.
• Design data schemas that enhance historical trend analysis, predictive modeling, and performance monitoring, rather than simply storing raw transactional data.
• Construct data warehouses and structured datasets that allow for efficient querying and insightful analysis, reducing the need for complex transformations downstream.
• Ensure data processing frameworks can accommodate both real-time updates and scheduled batch processing, supporting diverse analytical needs.
• Work closely with stakeholders across business functions to align data structures with operational goals, ensuring usability and relevance.
• Automate data ingestion and transformation using Python, SQL, and cloud-based technologies, streamlining data workflows.
• Implement data governance policies, ensuring compliance with security protocols, access management, and audit logging.
• Maintain and troubleshoot data pipelines, minimizing downtime and ensuring smooth data availability for reporting and analytics teams.
• Develop metadata and lineage tracking strategies, improving data transparency and usability across the organization.

• Develop and implement scalable data models that link SAP, Databricks, and Microsoft CRM, ensuring they support business intelligence, forecasting, and AI-driven analytics.
• Build robust data pipelines to extract, process, and structure information from SAP HANA, SAP BW, OData, and Microsoft CRM, ensuring accuracy and usability for analytical tools.
• Design data schemas that enhance historical trend analysis, predictive modeling, and performance monitoring, rather than simply storing raw transactional data.
• Construct data warehouses and structured datasets that allow for efficient querying and insightful analysis, reducing the need for complex transformations downstream.
• Ensure data processing frameworks can accommodate both real-time updates and scheduled batch processing, supporting diverse analytical needs.
• Work closely with stakeholders across business functions to align data structures with operational goals, ensuring usability and relevance.
• Automate data ingestion and transformation using Python, SQL, and cloud-based technologies, streamlining data workflows.
• Implement data governance policies, ensuring compliance with security protocols, access management, and audit logging.
• Maintain and troubleshoot data pipelines, minimizing downtime and ensuring smooth data availability for reporting and analytics teams.
• Develop metadata and lineage tracking strategies, improving data transparency and usability across the organization