• Design and implement modern data warehouse architectures • Lead migrations and modernization of existing DWH systems • Analyze and translate business and technical requirements into scalable, architecture-compliant solutions • Collaborate closely with internal stakeholders and cross-functional teams • Ensure data governance, security, and compliance with regulatory requirements in Switzerland • Further develop and optimize data platforms and reporting solutions • Define and enforce naming conventions, self-service approaches and scope boundaries; propose tools and produce guidelines

• Architect enterprise-wide data solutions spanning relational, flat file, IoT, and event-based sources • Design and build scalable data pipelines, leveraging modern cloud tools and best practices • Transform raw and OT data—sensor, time-series, and event streams—into reusable data products • Guide secure data movement across highly segmented and sensitive environments • Modernize legacy data patterns, recommending new approaches aligned to modern frameworks • Produce architecture diagrams and artifacts for governance and technical review boards • Collaborate with OT teams, architecture peers, and business data owners to drive adoption • Lead modernization initiatives and influence stakeholders through technical credibility • Research, evaluate, and recommend emerging technologies and best-fit architectural patterns

About 10a Labs: 10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely. About 10a Labs: 10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely. In this role, you will: - Design, implement, and optimize end-to-end data pipelines for scraping and processing structured and unstructured data using Google Cloud Platform (or similar) and best practices; - Conduct ad hoc web scraping and data collection to support research and intelligence initiatives; - Prepare data for further analysis, including data cleaning, transformation, anonymization, and masking; - Contribute to the development of internal and external APIs, following best practices; - Collaborate with ML engineers, other data engineers, and software developers to deliver actionable insights and functional tools, including internal and external dashboards, APIs, and data dumps; and - Drive other critical initiatives. Requirements: - Degree (or equivalent work experience) in Computer Science, Engineering, Information Science, Data Science or a related field (graduate degree preferred) - 2+ years of professional experience in data engineering or a closely related field - Ability to communicate complex technical ideas clearly to non-technical audiences - Proficiency in Python, SQL - Experience with web scraping/crawling (e.g., Beautiful Soup, Selenium, Scrapy) - Experience with Google Cloud Platform (or similar), including storage and database services (e.g., Cloud Storage, CloudSQL, Cloud Spanner) and workflow orchestration (e.g., Cloud Composer/Airflow, Cloud Run, Pub/Sub) - Experience building and managing data pipelines, especially for text data - Comfort working in fast-moving, high-impact environments, such as startups, AI research labs, or security-focused teams Compensation & Benefits: - Salary Range: $105K–$125K, depending on experience and location - Bonus: Performance-based annual bonus - Professional Development: Support for conferences, continuing education, or leadership training - Work Environment: Fully remote, U.S.-based - Health Benefits: Comprehensive health, dental, and vision coverage - Time Off: Generous PTO and paid holiday schedule Retirement: 401(k) plan

• Own the vision and technical roadmap for Omada’s enterprise data architecture, spanning ingestion, storage, modeling, and serving layers for analytics and applied statistics use cases. • Design, implement, and evolve scalable, secure, and cost‑efficient data solutions (datalakes, warehouses, marts, semantic layers) that support governed, cross‑functional analytics and self‑service. • Define and socialize architectural patterns, data contracts, and integration standards used by data and product teams across the organization. • Anticipate future needs (e.g., new product lines, new modalities, AI/ML workloads) and drive proactive architectural changes rather than reacting to incidents or point‑in‑time requests. • Lead the design of logical and physical data models to support enterprise metrics, dashboards, and ad hoc analytics, with a focus on reusability and clear ownership. • Implement robust data quality, validation, and monitoring frameworks that underpin trusted “single source of truth” definitions for core concepts (e.g., active member, MAU, GLP‑1 member).