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Strong brand, metro location, mid-level generalist data role, and broad tooling requirements drive high competition.
Data engineering skills are transferable, but adtech forecasting and measurement needs create moderate domain specificity.
Explicit 5–8 years plus mandatory streaming, data platform, cloud, and Databricks skills increase filter strictness.
Design and develop high-scale data pipelines and backend services for ad reporting, forecasting, and analytics.
Collaborate with product teams to build reliable streaming and batch processing systems using big data technologies (e.g., Spark, Flink, Kafka, Airflow).
Ensure data quality, operational excellence, and mentor junior engineers while supporting 24x7 platform availability.
Bachelor’s degree in Computer Science or related discipline.
5-8 years of software engineering experience focused on data-intensive systems.
Strong hands-on experience with distributed data processing frameworks (e.g., AWS Kinesis, Flink) and programming in two or more languages such as Java, Python, or Go.
Experience with data modelling, ETL best practices, big data architecture, data warehousing solutions like Databricks, and cloud services (especially AWS).
Experienced in building large scale, reliable stream and batch data processing platforms supporting ad tech or analytics use cases.
Operates well in cross-functional roles collaborating with product teams to translate business needs into technical systems.
Skilled in microservice architecture, containerization (Docker, EKS), CI/CD pipelines, and promoting engineering best practices and mentorship within teams.