Responsibilities
- Design and implement enterprise semantic models and certified KPI layers to ensure trusted, reusable business metrics.
- Build AI-safe data abstraction layers that prevent metric recomputation and ensure consistency across analytics and AI use cases.
- Develop and enforce data contracts, metadata standards, and semantic governance frameworks across domains.
- Engineer scalable batch and real-time streaming data pipelines in a modern cloud environment (GCP preferred).
- Collaborate with AI/ML teams to design reliable grounding strategies for AI applications and agents.
- Implement metadata management capabilities including cataloging, lineage, observability, and automated data quality checks.
- Apply DataOps principles including CI/CD, automated testing, and deployment automation for data products.
- Support domain-oriented and microservices-based data architecture patterns.
- Mentor engineers and promote best practices in semantic modeling, governance, and AI‑ready platform design.
Requirements
- 10–15+ years of experience in enterprise‑scale cloud data engineering and distributed systems.
- Strong hands‑on experience building modern data platforms in GCP (BigQuery, Dataform, Pub/Sub, Composer/Airflow, Cloud Run).
- Deep expertise in SQL, Python, and data modeling (dimensional modeling, lakehouse architectures).
- Experience with open‑source tech stack such as Iceberg, Trino, Kubernetes, Docker etc.
- Hands‑on experience with metadata management, lineage tracking, observability, and data quality frameworks.
- Experience building both batch and real‑time streaming data systems.
- Strong understanding of semantic modeling, business metric governance, and AI consumption patterns.
Hard Skills
- Data Modeling
- Dimensional Modeling
- Lakehouse Architectures
- Batch Data Systems
- Real‑Time Streaming Data Systems
- Data Quality Frameworks
- Lineage Tracking
- Observability
- DataOps Principles
- Automated Testing
Soft Skills
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