Data Engineer

• Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake)
• Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems
• Design scalable logical and physical data models for analytical and operational use cases
• Apply modern architecture patterns including event-driven and streaming architectures
• Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence
• Enable data serving layers (APIs, feature inputs, analytical endpoints) to support downstream systems

Senior Data Engineer

• Define how data is understood, accessed, and used by AI systems across the company.
• Build clean, reusable analytical data layers in BigQuery.
• Move business logic from BI into the warehouse.
• Define metrics, dimensions, and semantic consistency.
• Enable reliable AI ↔ data interaction.
• Design schemas + instructions so AI produces correct outputs.
• Test and refine real AI usage (not theory).
• Implement data-layer access control (not BI-layer).
• Ensure consistent behavior across BI, AI, and internal tools.
• Ensure metric reconciliation across different data sources.
• Prevent sensitive data leakage and uncontrolled query cost.
• Replace manual data workflows with AI-driven processes.
• Build agents for reporting, validation, and internal analytics.

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Senior Data Engineer

Senior Data Engineer