AI CoE Senior Data Engineer & Architect
QAD (company site, opens in a new tab)8+ yrsPuneOn-siteDirect career pageEst. 22–35 LPA
Posted 22h ago
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Job description
Structured overview of role & requirements
About This Role
- Own the data layer for AI lifecycle including assessing data feasibility, building and running AI platform data pipelines, and modeling semantic layers for agent queries.
- Ensure data correctness, currency, governance, and security with responsibility for pipeline health, freshness, quality monitoring, and cost management.
- Enable faster delivery of data-ready use cases by creating reusable data products, connectors, and standardized access patterns across AI agents.
Minimum Requirements
- 8+ years in data engineering with minimum 3 years building production data for ML, analytics, or Gen AI use cases.
- Strong skills in SQL, Python, and data pipeline orchestration tools like Airflow, dbt, or AWS Glue.
- Experience with cloud data platforms such as AWS (S3, Glue, Redshift, RDS), Azure, or Google BigQuery.
- Hands-on experience with semantic/dimensional modeling layers (e.g., Cube, dbt Semantic Layer, LookML, Power BI semantic models) and data security practices including role-based access and data masking.
Ideal Candidate Profile
- Experienced in production data pipeline ownership with focus on AI/ML and enterprise SaaS data sources like sales, customer, support, engineering knowledge, ERP, or manufacturing data.
- Skilled at collaborating with security, governance, system integrators, and data owners to implement governed, user-scoped data access and compliance.
- Operates strategically to build scalable, reusable data architecture supporting multiple AI use cases in a regulated enterprise environment with hybrid cloud infrastructure.