Data Engineer/Data Ops

Hello I am Adriano, Machine Learning Lead at Kayzen, and I am now looking for a Senior Data Engineer who will be a part of the machine learning engineering team bridging the gap between data science and ad systems engineering team

But wait, you have not heard of Kayzen before?

Kayzen is a mobile demand-side platform (DSP) dedicated to democratizing programmatic advertising. We enable leading apps, agencies, media buyers, and brands to run programmatic customer acquisition, retargeting, and brand performance campaigns through our self-serve and managed service options. Built on the three core pillars of performance, transparency, and control, Kayzen powers the world's best mobile marketing teams with bespoke solutions that fuel business growth and deliver a competitive advantage. With an unprecedented scale of 300B+ daily ad requests from 1.6B+ unique users worldwide, we serve up to 1B+ ads per day in 180 countries. Kayzen is accessible through our APIs and user interface.

The role

Are you excited about data? Will you take on the challenge to help us make Kayzen a ML first organization leading the AdTech space? Do you want to change the way we as an organization manage our data and do business? Are you interested in how billions of data points flow through various systems & data pipelines and how it is governed to generate knowledge and value? If your answer is "yes" to all these questions, if you are a problem solver and a team player, we would love to meet you!

Day to day

As a Data Engineer/Data Ops, you will work to create innovative solutions for handling peta-bytes of data with billions of rows & joins. Your work can vary from creating real time and offline features generation pipelines to managing our data infrastructure to be reliable and fast! You'll be responsible for:

• Program and maintain our data pipelines that fuels our on-premise/cloud data warehouse used to generate and serve our models
• Maintain and improve our fleet of data servers (the software), making sure they are reliable and able to process our billions of logs and data points
• Develop and productionize data pipelines for our ML models in both bare-metal and the cloud environment.
• Make suggestions and lead projects to improve our data processing capabilities
• Contribute to the team enabling us to be always better, * Exceptional career growth and learning opportunity
• A unique opportunity to be part of an experienced team of industry experts and entrepreneurs who bring massive change to the Adtech market
• Direct, day-to-day work experience with the management
• A fun, driven, and multinational team located across Germany, India, Argentina, Ukraine, Turkey, the UK and soon more countries
• A flexible work-from-home arrangement
• A 500-dollar home-office setup budget
• A 1000-dollar annual learning and development budget

Sounds like you? We are looking for a candidate with a minimum 5+ yrs of professional experience in creating and maintaining big data pipelines, identifying data related process improvements, maintaining Kubernetes, Hadoop and Spark infrastructure.

• Bachelor's/Master's degree in a quantitative field of Mathematics, Physics, Computer Science, Machine learning Engineering, Business Analytics, Information Management or related field.
• You know relevant programming languages (Python, Java, etc)
• Expert in SQL & NoSQL and big data processing pipelines (we use Python, Spark, Airflow)
• Have proven experience managing data infrastructure that can store and process Petabytes of data (we use Hadoop, Spark)
• Kubernetes wizard. LLM symbiotic.
• You have proven affinity with data
• You have strong analytical and problem-solving skills
• You can translate business requirements into data solutions
• You have excellent stakeholder management skills.
• Previous experience with Clickhouse is a plus
• Previous experience with ad-tech is a plus
• Experience with Real time big data processing is a plus
• General understanding of Machine Learning techniques (Neural Networks, Random Forest, etc.) and ML frameworks (Mlflow, PyTorch, Tensorflow, etc.) is a plus