Senior Data Engineer

About the Position As a Senior Data Engineer , you’ll play a key role in building and scaling the data infrastructure that powers our AI-driven platform. You’ll be responsible for designing, implementing, and optimizing reliable and scalable data pipelines that process large volumes of structured and unstructured data, from synthetic LLM prompts to large-scale web-scraped datasets, across a growing AWS-based data ecosystem. This role is focused on enabling rapid scale. Our data volume and traffic are increasing quickly as we expand to new AI channels and data sources, and we need robust, production-grade data systems that can keep pace with that growth. You’ll work closely with engineering, product, and go-to-market teams to ensure data is reliable, observable, and reusable across the organization. A core part of the role will be shaping the evolution of our data platform, including contributing to the design and implementation of our Data Lake architecture. You’ll help ensure our pipelines can handle increasing load, maintain high data quality, and support new product capabilities as we scale. You’ll also act as a trusted technical partner across teams, helping establish data best practices, improving operational reliability, and enabling teams to use data effectively in both product and business contexts. This role is remote in Germany. What You’ll Be Doing Design, build, and maintain scalable data pipelines that ingest, transform, and validate large volumes of data across multiple sources and channels. Improve the scalability, reliability, and performance of our data pipelines to support rapidly growing workloads and new data streams. Contribute to the design and implementation of our Data Lake architecture, enabling reliable data storage and reuse across teams. Manage and optimize data ingestion workflows, including data collected from web scrapers, third-party vendors, and internal systems. Monitor pipeline health, investigate incidents, and implement improvements to increase system reliability and observability. Support the onboarding and integration of new AI channels and data sources into the platform. Collaborate with teams across the organization to ensure data generated by different systems can be reused effectively for analytics and business intelligence. Identify and resolve performance bottlenecks in distributed systems, including rate limiting, concurrency, and throughput constraints. Advise engineering and product teams on data architecture, data quality, and best practices for managing scalable data workflows. Continuously evaluate and improve our data platform to support the company’s rapid growth and evolving product needs. Qualifications Strong experience building and operating scalable data pipelines in production environments. Hands-on experience working with Data Lakes or Data Warehouses (e.g., AWS Athena or similar technologies). Experience with data transformation and modeling. Strong experience working with AWS. Experience using Infrastructure-as-Code tools to manage cloud infrastructure. Proficiency in Python for data processing and automation. Experience working with distributed systems and managing large-scale data workflows. Experience implementing monitoring, observability, and incident response practices for data systems. Nice to have: Experience working with large-scale web scraping or external data ingestion systems. Experience supporting systems with rapidly increasing traffic or data volume. About Bluefish: Bluefish believes that AI represents the next major chapter of the internet – and that consumers will increasingly use AI to consume information and media online. On this new AI internet, brands will need new tools and technologies to tell their stories to consumers online – and a new marketing ecosystem will be created around AI. Bluefish is building the platform that helps brands engage consumers on this new AI channel, with powerful enterprise tools to manage AI brand safety and engage