• 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.

Role Description The Analytics Engineer is a technical producer role at the foundation of Loenbro’s data platform. This person designs, builds, and maintains the data pipelines, models, and architecture that make reliable reporting possible. They work within Microsoft Fabric to move raw data through the medallion layers — from ingestion to clean, structured, business-ready models — and hand off trusted datasets to the BI team to build reports against. - Design, build, and maintain data pipelines that ingest and transform data from source systems into the Loenbro lakehouse on Microsoft Fabric. - Integrate data from business applications into the lakehouse. - Enforce data quality standards — building validation logic, monitoring pipelines, and resolving data integrity issues before they surface in reports. - Use the medallion architecture — structuring bronze, silver, and gold layers to ensure data is clean, consistent, and ready for consumption by the BI team. - Build and maintain fact and dimension tables and semantic models that serve as the trusted, authoritative data layer for all reporting. - Collaborate with the team to understand reporting requirements and ensure the data model supports the metrics and KPIs the business needs. - Document data models, pipeline logic, and transformation rules so the team can understand, maintain, and extend the platform over time. - Manage deployments using SDLC and ALM best practices — version control, environment promotion, and change management. - Stay current on Microsoft Fabric and other reporting capabilities and proactively recommend improvements to the data platform architecture. Qualifications - Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or related field. - 1-3 years of experience in data engineering, analytics engineering, or a closely related technical discipline. - Excellent written and verbal communication skills. - Experience with data pipelines in a production environment. - Hands-on experience with medallion architecture (bronze/silver/gold layers). - Microsoft Fabric; Lakehouses, Data Warehouses, Dataflows Gen2, Data Pipelines, PySpark Notebooks, and OneLake. - SQL; complex queries, stored procedures, transformation logic, and performance tuning. - Python or another scripting language; pipeline automation, data transformation, and custom tooling. - Power BI experience – writing DAX measures and building semantic models, building visuals. Requirements - Power Platform experience (Power Automate, Power Apps). - AI/ML exposure — integrating AI-assisted pipelines or LLM outputs into data workflows. - Software development background in any additional language. - Microsoft DP-700 (Fabric Data Engineer Associate) or DP-600 (Fabric Analytics Engineer) certification. - Construction or heavy industry experience is a plus. Benefits - Medical, dental, and vision insurance. - 401(k) retirement plan with company match. - Paid time off (PTO) and holiday pay. - Life and disability insurance. - Professional development and training opportunities. - Employee assistance program (EAP).

Role Description Our Marketing organization is building an AI-powered intelligence system to drive strategy, insights, and revenue. We are looking for a Principal Analytics Engineer to lead the design and build of this foundation. This role is about more than just writing code—it’s about creating the semantic blueprint for how Elastic understands and interacts with its business data. You will synthesize complex data streams into a unified, high-fidelity system that serves as the "source of truth" for the entire customer journey. By engineering a structured knowledge layer, you will enable Elastic to scale Go-To-Market (GTM) efforts in a world where data must be optimized for human reporting, predictive science, and conversational AI alike. What You Will Be Doing - Architect the Foundation: Design and build the core BigQuery and dbt infrastructure that powers Elastic’s marketing intelligence, transforming raw signals into high-fidelity, agent-ready data products. - Enable AI & Agents: Develop the semantic layer and structured knowledge base that allows AI agents to accurately "talk" to our business data and reason through complex performance questions. - Map the Journey: Integrate disparate signals across digital, product, and sales into a unified lifecycle model that tracks the customer’s path from discovery to revenue. - Scale through Partnerships: Partner with Enterprise, Product, Sales, and Finance teams to align on shared metrics while mentoring other engineers to uphold high standards for our data foundation. Qualifications - Data-as-a-Product: You treat data as a high-value product. You are dedicated to the user experience of data—ensuring it is discoverable and reliable for both human teammates and AI agents. - Technical Proficiency: Deep experience with BigQuery, dbt, and semantic layers (e.g., dbt Semantic Layer, Vortex AI). You have a proven ability to apply automation or LLM-assisted workflows to the data modeling lifecycle. - Architectural Design: Ability to build complex, interconnected systems by starting with the desired outcome and working backward. You enjoy creating extensible frameworks that empower others to innovate. - Systems & Design Thinking: The ability to look at a complex web of data and see the underlying architecture required to make it simple and extensible. - Collaborative Communication: A track record of "translating" technical debt into business value and coaching peers through complex architectural hurdles. - Operational Excellence & Governance: You treat data as infrastructure. You have deep experience implementing data contracts, automated quality monitoring (DQM), and governance frameworks that ensure metrics remain consistent, secure, and reliable across the enterprise. Bonus Points - GTM Fluency: A strong understanding of Go-To-Market mechanics—knowing how technical data structures translate into business-critical concepts like customer acquisition, attribution, and revenue. - Marketing Science Foundations: Familiarity with Marketing Mix Modeling (MMM), causality, or incrementality analysis to help the business understand the true ROI of different channels. - Privacy & Ethics: Understanding of GDPR/CCPA compliance and how to manage data privacy and consent within a marketing stack, especially when training AI models. - Identity Resolution: Proven experience with Identity Stitching or Customer 360 frameworks to unify anonymous digital signals with known customer records. - AI Production Scaling: Experience moving AI models or agentic workflows from experimental pilots into standardized, production-level deployments. Compensation Compensation for this role is in the form of base salary. This role does not have a variable compensation component. The typical starting salary range for new hires in this role is: - $154,000 — $243,600 CAD Benefits - Competitive pay based on the work you do here and not your previous salary. - Health coverage for you and your family in many locations. - Ability to craft your calendar with flexible locations and schedules for many roles. - Generous number of vacation days each year. - Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service. - Up to 40 hours each year to use toward volunteer projects you love. - Embracing parenthood with a minimum of 16 weeks of parental leave.