Tapad is home to the team that cracked the code on cross-device marketing technology. Our groundbreaking, proprietary tech assimilates billions of data points to find the human relationship between smartphones, desktops, laptops, tablets, connected TVs and game consoles. With 91.2% data accuracy confirmed by Nielsen, Tapad offers the largest in-market opportunity for marketers and technologies to address the ever-evolving reality of media consumption across devices.

Are you an outstanding engineer who loves solving interesting challenges related to data at scale? Are questions such as ‘How to move multiple TB of data per hour, and optimally compress and serialize it?’ or ‘How do you ensure we are resilient to data loss in case of failure?’ what gets you excited? If so, this role may be a fit.

You will work with some of the brightest minds in the industry, along with a team that will continuously challenge you.

Responsibilities:

• Design and develop high-throughput, low-latency data processing pipelines to quickly ingest and make data available on the Tapad platform across various distributed data stores
• Analyze large datasets to identify opportunities to tune and improve the system
• Experiment with various Hadoop frameworks like Hive, Pig and Scalding to identify the optimal approach for extracting valuable insights from massive datasets

Requirements:

• Bachelors or Masters in Computer Science or related field
• 3+ years of experience ingesting, processing, storing and querying large datasets
• Professional Hadoop ecosystem experience, including storage optimization and job performance tuning
• Expertise in Java, Python or similar language(s). Functional programming experience is a plus
• Passion for code correctness and intuition about which values in data are to be expected in a business context

Tool We Use:

• Scala
• Hadoop (Hive, Pig, Scalding, Spark)
• Kafka
• MySQL, Redis, Vertica, Aerospike