• Own and maintain GCP-based data infrastructure including Cloud Composer, BigQuery, and Cloud Storage • Monitor pipeline health, troubleshoot failures, and ensure reliable, timely data delivery across the marketing technology stack • Build and maintain API integrations between marketing platforms • Provide architectural guidance and hands-on implementation support for data engineering projects within the Marketing Analytics team • Evaluate existing pipelines and integrations and identify opportunities to improve efficiency, reduce cost, and reduce technical debt • Support the development and automation of marketing data pipelines and reporting infrastructure • Research and recommend solutions for complex data flow and ETL/ELT challenges

• Build and maintain data pipelines to ingest, transform, and organize data from multiple sources • Work with clinical, claims, or similar structured datasets and map them into standardized data models • Run data quality checks to ensure accuracy and consistency • Use tools like Databricks, BigQuery, Redshift, dbt, and command-line utilities • Collaborate with cross-functional teams including data, product, and business stakeholders • Help maintain ongoing data refresh processes and troubleshoot pipeline issues when needed

Role Description We are seeking a clever and motivated Data Engineer to join our team in Europe. You’ll be the architect and maintainer of the pipelines, data models, and infrastructure that power our industry models, research, and consulting work. This role requires a mix of technical expertise, autonomy, and pragmatism—you’ll need to work independently, collaborate across our globally distributed team, and build systems that are both accurate, robust, observable, and modular. If you have a favorite SCD type (mine’s Type 2), we should probably talk. Key Responsibilities - Design, develop, and maintain robust and scalable ETL pipelines in Python to power our industry models and analytics products. - Work with lead analysts to ensure data accuracy, completeness, and utility value across multiple sources and formats. - Build scalable and reusable data workflows in cloud environments (GCP, AWS, or Azure). - Implement and maintain data quality monitoring. - Able to effectively use a SQL database via automated cron jobs. - Maintain and extend dashboards and APIs that deliver data to both internal analysts and external clients. - Support the integration of new datasets, tools, and infrastructure components to enhance our analytics capabilities. Qualifications - 1–3 years of experience in a Data Engineering, Data Science or reasonably equivalent role. - Capable in Python, SQL, and Excel. - Strong ETL development experience. - Hands-on experience with at least one cloud platform (GCP, AWS, or Azure). - Highly autonomous—able to take a problem from definition to deployment with minimal oversight. - The right combination of opinionated and low-drama. Requirements - Experience with Flask, Redis, Dash, Airflow, GitHub Actions, and/or Kubernetes. - Familiarity with automated regression, smoke, or unit testing methodologies. - Experience working with messy, real-world data. Location Full-time role based remotely in Europe.

• Design, develop and maintain corporate data platforms that enable insights from customer data • Apply expertise in SQL and Python to execute projects and tasks with high quality, high accuracy and within established deadlines • Design, code, test, debug and document programs and scripts using agreed standards and tools, ensuring well-structured deliverables • Ensure data quality and implement tools and frameworks to automate the detection of data quality issues • Profile data sources and develop ETL processes, with a solid understanding of data modeling fundamentals, using SQL, Python and ETL/ELT support tools • Assist management in preparing estimates and proposals for clients • Plan effective solutions for data storage, security, sharing and publishing across the organization • Create and update documentation of process flows and business rules • Maintain existing pipelines and process large volumes of data as required