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Tier-1 brand and metro location increase applicant density, but senior specialized profile moderates competition.
Core data engineering and platform skills are broadly transferable across industries.
Explicit 10+ years plus mandatory large-scale data platform, distributed processing, cloud, and ML production experience.
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Architect and build large-scale data platform systems processing billions of events, including pipelines, data lakes, and serving infrastructure.
Develop foundational infrastructure to enable AI and ML capabilities, such as feature platforms, training data pipelines, and model-serving.
Set technical standards and lead engineering strategy, mentor engineers, and drive cross-team initiatives in data engineering and applied AI.
Minimum 10 years of software engineering experience building scalable software systems.
Proven experience designing and shipping production-grade, large-scale data platforms.
Strong programming skills in Python, Scala, or Java with knowledge of distributed data processing frameworks like Spark, Flink, Kafka.
Hands-on experience with cloud data infrastructure (AWS or GCP), data lakes, warehouses, and production ML/AI systems.
Experienced technical leader with ability to set architecture direction and mentor teams across multiple groups.
Background in designing data platforms that integrate ML/AI pipelines and model serving.
Comfortable operating in a fast-growing, high-scale streaming or media platform environment with large data volumes.