AWS, Azure, Big Data Technology, Data science techniques, Docker, Google BigQuery, Google Cloud Platform (GCP), Kubernetes-K8s, Leadership Skill, Machine learning techniques, NoSQL, Python Programming, Scala Programming, SQL, Teamwork
What will you do:
As a Manager of the Data Platform team, you will be taking a central role orchestrating, overseeing and leading the many different aspects of our challenging journey towards a new and modernized big data Lakehouse platform (PB’s of data), built using Databricks on Google cloud (GCP). Among the challenges - ingestion at stream in massive scale, providing a platform for processing structured and unstructured data, security and compliance at Enterprise scale, data governance, optimizing performance, storage and cost and many more..
You will lead, mentor, guide, recruit and manage a team of experienced Data Engineers and be responsible for the enablement of our Big Data platform serving developers, data engineers, data analysts, product managers, data science and ML Engineering
You will work closely with our Data PM on leading our Data Strategy and as such, you will learn how data serves our goals, come up with ways to improve our TBs of daily data processes while maintaining high data quality; guide other R&D teams and provide best practices; conduct POCs with latest data tools; and by that, help our clients make smarter decisions that continuously improve their ad-impression quality.
FInd your way to influence and impact a team that utilizes a wide array of languages and technologies, among them - GCP, Databricks, Spark, Python, Scala, SQL, BigQuery, Vertica, Kafka, Docker, Kubernetes, Terraform, Prometheus, Gitlab and more.
Who you are:
DoubleVerify is a big data and analytics company. We track and analyze tens of billions of ads every day for the biggest brands in the world like Apple, Nike, AT&T, Disney, Vodafone, and most of the Fortune 500 companies. If you ever saw an Ad online via Web, Mobile, or CTV device then there are big chances that it was analyzed and tracked by us.