Lead Data Engineer, Kargo

• You have proven expertise in owning, designing and maintaining large-scale, interdependent systems
• You have experience in planning deployment and leading a team to complete development on such systems
• You have mastery in Python and experience with building complex pipelines in Airflow
• You have proficiency in crafting SQL queries for Snowflake, with an understanding of cost and performance nuances
• You have experience with using Spark to process large volumes of data and understand the nuances of how to structure data for efficient querying
• You have experience with Docker/Kubernetes and the AWS ecosystem
• You have familiarity interacting with third party APIs within ETL pipelines
• You are familiar with Agile methodologies and have worked with Jira/Confluence
• You are comfortable communicating and presenting complex topics and plans to people in the organization, regardless of their technical skill level
• You are comfortable with requirements gathering, cross-team design brainstorming sessions, and ensuring consensus among stakeholders
• You have an innate drive to take initiative and ownership of projects, transform uncertainty into actionable strategies, and a deep-seated pride in delivering meticulous, high-quality work
• Experience with identity and data products within AdTech is a big plus

• At Kargo, we are at a pivotal juncture
• As we navigate the international expansion landscape and the imminent cookie-less era of AdTech, we are intensifying our focus on identity and contextual targeting solutions
• We seek a proactive Lead Data Engineer who thrives in transforming ambiguity into clarity
• Your primary role will be to architect, design, and optimize the data structures and processes integral to enabling fast and accurate identity targeting
• While maintaining a hands-on approach, you will also lead and mentor a dedicated team of data engineers, ensuring the delivery of high-quality, scalable solutions
• Oversee a team of data engineers, ensuring adherence to high standards, project delivery, and continuous skill development
• Engage in deep-dive sessions with a diverse range of technical and non-technical stakeholders, translating business needs into actionable technical requirements in Jira/Confluence
• Collaborate with data vendors and our data partnership team to evaluate and onboard new datasets that will enhance our targeting abilities. You and your team will be responsible for building and maintaining scalable and cost-efficient data ingestion pipelines (Python, Airflow, Snowflake, AWS, Docker, Kubernetes)
• Spearhead the architecture, design and enhancement of our targeting solutions and workflows, ensuring quality, efficiency, and transparency of the processes
• Create comprehensive documentation detailing system architecture, interdependencies, data flow diagrams, and best practices for future reference and onboarding purposes
• Monitor data storage and processing costs, seeking ways to optimize without compromising on quality

The way in which advertisers reach out to their audiences has changed dramatically over the last decade, primarily due to the invention of the smartphone. Whilst mobile ads have existed since the beginning of smartphones, most rely on technology that was built for desktops, resulting in bad advertising and missed opportunities.