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Tier-1 brand, metro location, mid-level generalist data role with broad skill requirements drives high competition.
Data engineering skills transfer broadly, but large-scale streaming/media experience biases background fit.
Explicit 2–4 year requirement, mandatory data engineering focus, and specific tech stack used increases shortlisting strictness.
Build and maintain critical data infrastructure handling hundreds of millions of requests per minute and petabytes of data.
Develop scalable, reliable, and performant data pipelines supporting AI model training and autonomous AI Agents.
Optimize ingestion, storage, and processing components contributing to real-time data pipelines and system performance.
2-4 years of professional software engineering experience with at least 2 years in data engineering or related fields.
Hands-on experience with data processing frameworks (e.g., Apache Spark, Apache Flink) and messaging/streaming platforms (e.g., Apache Kafka, Kinesis).
Familiarity with major cloud platforms (preferably AWS) and associated data services; strong SQL skills and experience with relational and NoSQL databases.
Proficiency in at least one programming language such as Python, Java, Scala, or Go.
Experienced in handling large-scale, high-throughput data systems with millions of events and terabytes of data.
Comfortable working under guidance within a senior engineering team to develop and optimize data pipelines for AI and real-time event processing.
Possesses a foundational understanding of distributed systems and data processing patterns suitable for scalable data infrastructure.