Dice is seeking a highly skilled Data & AI Engineer with strong expertise in Microsoft Fabric, Azure AI Services, data engineering, and AI-powered analytics solutions. The ideal candidate will design and manage scalable experimentation data platforms and develop AI-powered data agents to support enterprise analytics and experimentation workflows.
Responsibilities:
- Design, build, and maintain curated Silver-layer datasets in Microsoft Fabric for experimentation analytics and reporting
- Develop reusable data products including standardized tables and views for dashboards, scorecards, and ad hoc analysis
- Ensure datasets are analysis-ready, cleaned, deduplicated, and semantically aligned
- Conduct gap assessments across experimentation KPIs, telemetry systems, and Silver-layer datasets
- Identify data quality, latency, schema, and join-key issues and recommend remediation strategies
- Improve data models using: Facts and dimensions, Conformance rules, Surrogate keys, Grain optimization
- Conduct workshops with BI, reporting, experimentation, and engineering teams
- Define KPI calculations, attribution logic, governance standards, and refresh SLAs
- Create source-to-target mappings, data dictionaries, validation rules, and technical specifications
- Build and maintain robust data pipelines using: Microsoft Fabric Pipelines, Azure Data Factory (ADF), Telemetry/1DS pipelines
- Implement orchestration, incremental loads, monitoring, and error handling mechanisms
- Validate and reconcile datasets with Adobe Customer Journey Analytics (CJA)
- Build automated validation routines for: Missing data, Duplicate detection, Schema drift, Metric anomaly detection
- Coordinate issue resolution with telemetry and engineering teams
- Design and build AI-powered data agents using: Fabric Data Agents, Copilot, Azure OpenAI
- Enable use cases such as: Automated scorecards, Narrative summaries, Self-service analytics Q&A, Metric definition assistants, Data quality monitoring assistants
- Define AI agent architecture, RBAC security, grounding sources, and evaluation metrics
- Maintain documentation for: Data transformations, Pipeline dependencies, Metric definitions, Validation rules
- Implement best practices for: Naming conventions, Semantic consistency, Versioning, Cost optimization
- Support operational monitoring, troubleshooting, and continuous improvement initiatives
Requirements:
- Strong expertise in Microsoft Fabric
- Strong expertise in Data Engineering
- Strong expertise in Data Modeling
- Strong expertise in Big Data Platforms
- Strong expertise in Data Pipeline Design
- Hands-on experience with PySpark
- Hands-on experience with Azure AI Services
- Hands-on experience with AI/ML Integration
- Hands-on experience with Microsoft Fabric Data Agents
- Hands-on experience with Azure Data Factory (ADF)
- Strong understanding of Clickstream data
- Strong understanding of Customer Analytics
- Strong understanding of Adobe Analytics
- Strong understanding of Adobe Customer Journey Analytics (CJA)
- Strong understanding of Experimentation data platforms
- Experience with Data validation and reconciliation
- Experience with Performance tuning and monitoring
- Experience with Telemetry pipelines
- Experience with Data governance and semantic modeling
- Strong analytical and problem-solving skills
- Ecommerce domain experience preferred
- Experience supporting experimentation and customer analytics platforms
- Strong knowledge of ecommerce and digital analytics ecosystems
- Exposure to AI-driven analytics and self-service data platforms
- Strong collaboration and stakeholder communication skills