AI Data Engineer

Good to have skills: SQL, Python, Apache Spark, Airflow, Data Modeling, data engineering, ai
Key Responsibilities:
Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and curate data from multiple sources.
Develop and optimize data models and curated datasets to support analytics, reporting, and AI/ML workloads.
Implement data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and reliability.
Enable GenAI initiatives by preparing high-quality datasets for downstream consumption (e.g., feature-ready and retrieval-ready data).
Collaborate with cross-functional teams to gather requirements, define data contracts, and deliver reusable data assets.
Troubleshoot pipeline failures and performance bottlenecks; improve scalability, latency, and cost efficiency.
Maintain documentation for pipelines, transformations, lineage, and operational runbooks to support maintainability. Minimum Qualifications:
BTECH, MTECH, MCA, MSC or equivalent education.
3–5 years of experience in data engineering with hands-on ownership of production-grade pipelines.
Strong experience in ETL processes including extraction, transformation, orchestration, and scheduling.
Working exposure to GenAI-oriented data preparation needs and supporting AI/ML data workflows.
Ability to collaborate with stakeholders to translate requirements into scalable data solutions. Preferred Qualifications:
Experience designing scalable data architectures and implementing reusable data frameworks for multiple use cases.
Familiarity with building datasets for GenAI use cases such as retrieval workflows and knowledge augmentation patterns.
Proven ability to improve pipeline reliability through automation, alerting, and proactive monitoring.
Strong problem-solving skills with a track record of optimizing transformations and reducing end-to-end processing time.
Experience working in agile teams and contributing to code reviews, documentation, and engineering best practices.

Work arrangement
No