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Our enterprise clients are moving from fragmented data foundations to AI-first data platforms capable of supporting large-scale, business-critical AI systems. AI performance is directly constrained by data quality, availability, governance, and latency. This role exists to build and operate the data backbone that enables reliable, scalable, and compliant AI at enterprise scale.
Job Responsibility:
Design, build, and maintain scalable batch and streaming data pipelines
Implement data ingestion from heterogeneous enterprise sources (databases, APIs, events, files)
Structure data for downstream AI and ML consumption
Ensure data quality, consistency, and availability across environments
Build and operate feature stores and analytical data layers for ML
Design data models optimized for training and inference workloads
Enable efficient data access patterns for real-time and near-real-time AI use cases
Support experimentation while enforcing production-grade standards
Translate business and AI requirements into robust data architectures
Collaborate closely with AI/ML Engineers, MLOps, Infra, Security, and Product teams
Implement monitoring, validation, and alerting on data pipelines
Design lifecycle strategies for data versioning, backfills, and schema evolution
Requirements:
Strong background in data engineering for large-scale systems
Proven experience delivering production-grade data pipelines
Familiarity with enterprise data landscapes and constraints
Engineering-first approach to data
Strong ownership and accountability for data reliability
Comfortable operating in complex, multi-stakeholder environments
High standards for robustness, scalability, and maintainability