AI Data Engineer

About the job: Join a team where data engineering meets intelligent automation to power next-generation analytics and AI experiences. In this role, you’ll help build reliable, scalable data pipelines and enable GenAI-ready datasets that support experimentation, model development, and business decision-making. You’ll collaborate closely with analysts, data scientists, and platform teams to turn raw, fragmented data into trusted, well-modeled, and accessible assets. If you enjoy solving complex data challenges, improving performance, and bringing structure to fast-moving AI initiatives, this is a great opportunity to grow your impact. You’ll work in a culture that values ownership, continuous learning, and practical innovation—where clean data, strong engineering, and thoughtful collaboration come together to deliver real outcomes. Technical Requirements: Good to have skills: SQL, Python, Apache Spark, Airflow, Data Modeling, data engineering, ai Responsibilities: 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 Skills: Technology->Data Engineering->Databricks,Technology->AI-Responsible AI->Responsible AI->explainable ai