• As the Sr. Engineering Manager for Analytics Engineering, you will lead the evolution of Rula’s data landscape, transforming raw information into the strategic engine that powers our mission of accessible mental healthcare. • Managing a high-performing team of senior engineers, you will act as a "Builder-Leader," architecting production-grade ELT pipelines and a robust semantic layer that enables self-serve insights for our Product, Finance, and Clinical teams. • Partner deeply with cross-functional stakeholders to bridge the gap between complex engineering and actionable business logic, ensuring that every decision at Rula is backed by reliable, high-integrity data.

• Design and implement robust dimensional and relational data models that support analytical use cases across Product, Marketing, Operations, and Finance. • Build and maintain scalable dbt transformation pipelines, ensuring high data quality, performance, and cost-efficiency from raw ingestion to business-ready outputs. • Own the transformation and modeling of curated (Silver/Gold) datasets, ensuring clear contracts and traceability from raw to business-ready data. • Collaborate with data analysts, product analytics, data scientists, and business stakeholders to translate requirements into durable data products that support experimentation, A/B testing, and advanced analytics. • Implement data quality tests, monitoring, SLAs, and alerting to ensure reliability of critical analytical datasets. • Enhance our LLM development support capabilities – creating tools / skills / agents that give our LLMs more context and help us continually improve their abilities to debug, create code, and maintain systems. • Partner with Data Engineers to define and enforce data contracts, ensuring schema stability and minimizing downstream breakage. • Establish and evangelize analytics engineering best practices, including version control, code review, testing standards, and documentation. • Empower self-service analytics by building intuitive, well-documented data marts and semantic layers.

• Entwickelst das Data Warehouse von einer funktionierenden MVP-Landschaft hin zu einer skalierbaren, belastbaren und zukunftsfähigen Plattform • Übernimmst technische Ownership für das Data Warehouse und entwickelst dessen Architektur, Datenmodelle und Qualitätsstandards gezielt weiter • setzt dbt strategisch ein: Von modularer Modellierung über Tests, Dokumentation und Refactoring bis hin zu wiederverwendbaren Patterns mit Jinja und etablierst Best Practices im Team • Verbessern die Performance, Skalierbarkeit und Zuverlässigkeit unseres Redshift-basierten Warehouses • Entwickelst neue Datenpipelines und verantwortest gemeinsam mit dem Team deren saubere Orchestrierung und stabilen Betrieb in Airflow • Bringst Best Practices in Governance und Observability ein, entwickelst Standards für Datenqualität, Ownership, Monitoring und Dokumentation weiter.

• Data Modelling & Transformation: Lead the development and maintenance of our dbt-enabled data models, transforming raw data into Silver and Gold layers within our Snowflake Medallion architecture. • Product Delivery: Build and optimise the data structures that power our customer-facing BI products, ensuring high performance and accuracy for our external users. • AI Integration: Architect and deliver high-quality data sets specifically designed to be consumed by both core applications, AI features and LLM-driven functionalities within the Arbor suite. • AI-First Workflow: Embrace and pioneer the use of AI-assisted engineering tools to accelerate planning, architecting, and coding tasks, setting the standard for efficiency within the team. • Collaboration: Work closely with Warehouse Data Engineers to ensure upstream data quality. • Domain Exploration: Partner with Product Managers and domain experts to translate business requirements into technical schemas, bridging the product teams into the data function. • Data Integrity: Support the wider data team in maintaining rigorous data quality and observability standards, utilising Datadog and dbt tests to ensure one version of the truth. • Mentorship & Best Practice: Act as a Senior pillar for the analytics engineering community, performing code reviews and championing best practices.