AI Data Engineer
Role Summary: AI Data Engineer with 5–8 years of experience building scalable data pipelines to support Data science models. Requires strong Streaming data and Spark/Python skills, solid experience with distributed data processing, and the ability to deliver reliable data systems for batch and real-time workloads. AdTech experience is a plus.
Key Responsibilities: Build and maintain batch and real-time data pipelines supporting Data Science, analytics, and operational use cases. Develop scalable data models, ETL/ELT pipelines, and distributed processing jobs across structured and unstructured data. Implement ingestion, transformation, streaming, storage, and data quality solutions using Spark, Kafka, Python, and modern data frameworks. Partner with product, engineering, analytics, and data science teams to deliver reliable, privacy-aware, and cost-efficient data platforms.
Required Skills: BS/MS in Computer Science, Engineering, Data Science, or related field. 5–8 years in data engineering, software engineering, or platform engineering with strong experience building scalable data pipelines and distributed systems. Strong proficiency in Spark and Python, with hands-on experience in production-grade data engineering and cloud-based data platforms. Hands-on with Spark, Kafka, HBase, Presto, Hive Flink, Airflow/Beam, SQL/NoSQL, cloud platforms, and AI/ML data enablement. Success Traits: Ownership, hands-on execution, strong problem solving, collaboration, and the ability to build scalable data solutions with high reliability and quality.