Adge is revolutionizing brand marketing by transforming performance alignment to unlock hidden potential. Over 90% of brand content currently falls short of profitability, but Adge bridges this gap through cutting-edge technology. Utilizing comprehensive data analysis and AI, we deconstruct marketing content into key attributes to create data-driven strategies that boost performance. We are replacing guesswork with advanced analytics, allowing brands to achieve greater impact and reduce costs.

We are seeking a Senior Data Engineer to join our team to help build and evolve our data infrastructure. This role is ideal for an engineer who thrives in a collaborative environment, is comfortable leveraging the latest AI-assisted tooling, and wants to take ownership of key features within our data pipelines. You will focus on building high-impact data assets that fuel our AI models and decision-making systems.

• Feature Ownership: Take full ownership of specific data pipeline components, from development and testing to deployment and maintenance.
• Build AI-Driven Pipelines: Leverage modern AI tools and frameworks to accelerate data processing, transformation, and ingestion workflows.
• Develop Data Assets for AI: Collaborate with data scientists to architect and implement data pipelines tailored for training and inference, ensuring high-quality inputs for our models.
• Data Observability: Implement proactive monitoring, data quality checks, and automated alerting to ensure the reliability of our data assets.
• System Optimization: Refine existing workflows for performance and cost-efficiency, utilizing cloud-native tools and best practices.
• Collaborative Development: Work closely with cross-functional teams to integrate new data sources and ship features that enhance our overall data ecosystem.
  

• 6+ years of experience in Data Engineering.
• Formal education in Computer Science or a related field.
• Strong Python proficiency: Expert-level skills in writing clean, maintainable, and efficient code for data processing.
• Modern Data Stack experience: Proficiency with analytical data stores (BigQuery, ClickHouse, Snowflake, or similar).
• AI/ML Integration: Proven ability to build pipelines that integrate with AI workflows (e.g., handling embeddings, vector data, or pre-processing unstructured data for ML).
• AI-Assisted Workflow: Demonstrated comfort using LLM-based coding assistants and modern development tools to increase velocity and maintainability.
• Streaming & Messaging: Experience with event-driven architectures (Kafka, Pub/Sub).
• Cloud Native: Hands-on experience with GCP or AWS.
• Orchestration: Proficiency with modern orchestration tools (Airflow, Dagster, or Prefect).
• Data Modeling: Strong understanding of schema design, performance optimization, and creating reliable, scalable datasets.
• Environment: Experience in AdTech or a high-growth startup environment is a strong advantage.