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Strong brand, metro location, and broad technical requirements increase candidate competition.
Core data engineering skills are highly transferable across industries despite media-specific domain knowledge preferred.
Multiple explicit years requirements and mandatory tech stack create strict shortlisting filters.
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Lead design, implementation, and maintenance of high performance big data infrastructure and processing pipelines handling billions of events daily.
Integrate and automate ingestion, transformation, and augmentation of diverse data sources to create a unified customer view enabling measurement, targeting, and revenue growth.
Mentor data engineering team, collaborate cross-functionally, and provide technical leadership and roadmap updates to senior leadership.
Bachelor's degree in Computer Science or related technical discipline.
8+ years experience designing and developing big data processing systems using distributed computing.
6+ years experience in SQL and working with cloud platforms like AWS; 5+ years in Python and Spark; 4+ years with Apache Airflow.
Expertise in optimizing complex SQL queries, experience with NoSQL (DynamoDB), networking protocols (HTTP/HTTPS, JSON, REST APIs), and object-oriented programming (e.g., Java).
Experienced leader in scalable big data systems with deep expertise in AWS cloud services and data pipeline automation.
Strong hands-on skills in distributed computing, data modeling, and complex SQL optimization suited for high-volume, structured and unstructured data.
Comfortable operating in a high-profile, cross-functional environment requiring clear communication and mentorship of engineering teams.