Senior Data Infrastructure Engineer

Rise is revolutionizing programmatic digital advertising with AI-powered auction optimization, driving greater efficiency in real-time bidding (RTB). Our algorithms ensure partners maximize revenue by reaching the right audience at the right moment.

At Rise, we’re on a mission to redefine how digital advertising works, and we’re doing it at scale with innovative solutions trusted by some of the largest industry leaders and top publishers across CTV, in-app, and web.

What makes us different? It’s our people. We believe happy employees make for exceptional work, so we prioritize creating a workplace where you’ll feel inspired, valued, and excited to start your day.

We are looking for a Senior Data Infrastructure Engineer to join our dynamic Data Engineering team at Rise, a data science-driven ad-tech company. You will play a key role in designing and optimizing robust, scalable, and efficient big data infrastructure to support AI-driven decision-making and analytics. In this position, you’ll collaborate with cross-functional teams in a highly technical and fast-paced environment, working with modern cloud technologies to solve challenging problems and drive impactful results.

Responsibilities:

• Design, build, and maintain scalable, reliable data pipelines and AWS-based infrastructure (EMR, S3, Redshift, Glue, RDS, Spark) for Rise’s ad-serving platform.
• Develop and optimize real-time streaming data workflows using tools like Kafka, Kinesis, or MSK.
• Optimize data pipelines for performance, low latency, high throughput, and cost efficiency.
• Work closely with data engineers, data scientists, ML ops, analysts, and other stakeholders to understand data needs and improve data processes.
• Automate workflows to ensure high performance in production and development environments.
• Monitor and maintain data systems, ensuring uptime, reliability, and stability.

Requirements:

• At least 3 years of experience as a Data Engineer or in a similar data infrastructure role.
• Strong hands-on experience with AWS data infrastructure (EMR, S3, Glue, Spark) and Google Cloud services (BigQuery, GCS, Dataflow, Pub/Sub).
• Experience with data processing frameworks such as Apache Spark and streaming tools like Kafka, Kinesis, Spark Streaming, Flink, or MSK for real-time data ingestion and transformation.
• Proficiency with tools like Airflow for managing workflows and Python for building and optimizing data workflows.
• Expertise in optimizing data pipelines for performance, cost efficiency, and scalability in cloud environments (AWS, GCP, etc.), including networking and VPC setups.
• Strong communication skills and the ability to collaborate effectively with cross-functional teams.

Advantages:

• Hands-on experience with Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Pulumi.
• Familiarity with MLOps methodologies and tools.
• Experience with version control and automated testing in data workflows.