• Own Landing's dbt models, data pipelines, and warehouse architecture. • Maintain dbt model library — keeping models clean, tested, documented, and aligned with business data usage. • Manage Landing's ELT stack — including Stitch, Snowflake, dbt, and associated monitoring and orchestration tooling. • Establish a finance and operations data spine — authoritative source of truth for core executive KPIs, metric definitions, and warehouse logic. • Develop analytics engineering standards, tooling decisions, and long-term roadmap for data platform. • Foster cross-functional data partnerships with Engineering, FP&A, and Accounting — translate between technical infrastructure and business need. • Enforce data quality standards, testing frameworks, and metric governance practices. • Support executive, board, and investor reporting — work directly impacts high-stakes decision-making and fundraising.

Role Description To have a material strategic and financial impact on our clients, we need to have an Analytics Engineering team able to consistently do the best work of their careers for each client. The environment is complex: a multi-platform stack spanning GTM, GA4 360, Quantum Metric, Dynamic Yield, performance marketing pixels, CRM tracking, server-side event pipelines, and a headless Shopify architecture. The right candidates will be technically strong, comfortable with ambiguity, and able to collaborate closely with product and engineering teams to unblock implementation and drive tracking quality at pace. - Tag Implementation & Backlog Clearance: Day-to-day implementation, QA, and documentation of tags across GTM, GA4, Quantum Metric, Dynamic Yield, performance marketing pixels (Meta, Google), CRM, and server-side event pipelines. Clear and manage an active backlog with a focus on speed and accuracy. - Figma-to-Data-Layer Scoping: Translate new feature designs into detailed data layer specifications, defining parameters, values, and formats for developers. Own the tracking scope from Figma through to QA sign-off. - Developer Collaboration: Work closely with product and engineering teams to unblock data layer implementation, reduce friction during dev cycles, and ensure tracking fires correctly across releases without breaking downstream platforms. - Cross-Platform Sessionization & Attribution: Support and maintain tracking consistency across web, native app (Flutter), and Shopify checkout, including custom app sessionization logic, UTM persistence, and attribution integrity. - Performance Marketing Pixel Integrity: Monitor and protect Meta and Google pixel event scores, which directly inform ad spend optimization across hundreds of millions in media budget. Prioritize critical pixel fixes above internal data issues. - Server-Side Tracking: Maintain client-side and server-side event sync via the LR platform, ensuring consistent 200-return rates and parity between client and server event pipelines. - A/B Testing Infrastructure Support: Maintain DY-to-QM event piping (dyrc, dy2qm), variant tracking events, and feature flag reporting to support experimentation velocity. - Monthly Data Audits: Participate in monthly data audits comparing GA4 events against the source of truth. Triage breaks between GTM hot-fixes and developer ticket resolution, and document findings. - Consent Management Compliance: Support OneTrust → GTM consent enforcement following strict change management protocols involving security and legal stakeholders. - QA Framework Development: Contribute to an emerging agentic QA framework using AI-assisted assertion testing against BigQuery downstream data. - Intake & Refinement Participation: Join weekly refinement meetings to assess incoming epics and tickets for data tracking impact, applying domain analytics labels and scoping new tracking requirements. - Solution Design Document (SDD) Maintenance: Contribute to and maintain the centralized SDD, the shared source of truth across engineering, product, and analytics used to govern all tracking. Qualifications - Tag Management Experience: 3–5+ years of hands-on tag management in a DTC or e-commerce environment. - Google Tag Manager (GTM): Strong proficiency in GTM, including implementation, governance, consent enforcement (OneTrust to GTM), hot-fix process, and tag structure best practices. - GA4: Solid experience with GA4 including custom dimension governance, event schema management, and intentional reuse of dimensions to control BigQuery event volume and downstream data quality. - Performance Marketing Pixel QA: Demonstrated ability to QA Meta CAPI and Google Ads event scores and understand how data layer accuracy directly impacts ad optimization. - Figma-to-Data-Layer Translation: Ability to scope tracking requirements from Figma designs into developer-ready data layer documentation, technically fluent enough to collaborate with engineers and connect to business outcomes. - Cross-Platform Tracking: Experience maintaining tracking consistency across web, native mobile app, and Shopify checkout, including sessionization, attribution, and server-side/client-side parity. - Shopify Data Layers: Familiarity with Shopify data layer constraints, checkout instrumentation limitations, and nuances like automated discounts, compared price fields, and Shop Pay redirect behavior. - Developer Collaboration: Proven ability to work alongside engineering teams, unblock data layer implementation issues, and scope tracking tickets clearly in Jira. - Documentation: Strong documentation habits with experience maintaining centralized, cross-functional tracking documentation. Requirements - Bachelor’s degree in Computer Science, Software Engineering, or a related field. - Server-Side Tracking Platforms: Experience with platforms like LR (Elevar) or similar for managing client/server-side sync and 200-return rate monitoring. - BigQuery: Experience with BigQuery event volume management and downstream pipeline health monitoring. - Headless Site Architecture: Familiarity with headless/server-side rendered environments and client-side event capture patterns. - App Tracking: Experience with native app tracking (Flutter or similar), including custom sessionization logic and web/app event parity approaches. - Agentic QA: Interest in or experience with AI-assisted QA frameworks (e.g., Claude, ChatGPT, or Gemini) for automated assertion testing against analytics data. - Comfort working in a fast-paced, backlog-driven embedded team environment with multiple stakeholders across analytics, product, engineering, and legal. - Solid coding foundation. You can collaborate with internal and external SMEs to problem solve and create smart solutions. - Expectations Wizard. You always under-promise and over-deliver. - Agile. You have an innate ability to embrace change and quickly adapt to new situations, changes in direction and competing priorities within a results-driven environment. You know how to learn fast with the goal of operating independently without sacrificing a quality output. - Proactive. You are experienced at identifying risks & resolving issues, escalating as needed to senior leadership across an entire portfolio of client projects. - Live in the details. Solid problem-solving skills with a high attention to the little things. You bring clarity to chaos. - Are confident, yet kind and low on ego. 'Nuff said. - Wear many hats. Demonstrated ability to manage multiple, simultaneous projects and manage programs. - Well rounded. Have experience with various digital projects. - 1+ years of experience working in an agency or client-facing environment. - Prior experience in DTC, fashion, or high-growth e-commerce strongly preferred. - TPM-adjacent skills: Ability to manage intake, scope tickets, and facilitate cross-functional tracking discussions without formal project management authority. Benefits - Full-time position - Work from Home - Prepaid Medicine: Your health is our priority! - Life Insurance: Peace of mind for you and your loved ones! - Indefinite-Term Labor Contract: Stability and all legal benefits included!

• Report to the team lead and be a key contributor to our migration from a legacy SQL Server data stack to a modern cloud data stack consisting of Snowflake, dbt and Tableau**• Design and build data pipelines using Snowpark and/or Snowpipe where native connectors do not exist for Snowflake for various data sources**• Contribute to designing and building a new multi-dimensional data model in Snowflake**• Work closely with our Business Intelligence team - monitoring and implementing enhancement requests for the data warehouse to meet the reporting demands of stakeholders The successful candidate will also be given the opportunity to develop mentoring skills as we look to expand the team’s skillset to match those of our counterpart US team. As we work collaboratively with team members in different timezones, it is important that the candidate has strong interpersonal skills.

• Build and maintain high-quality LookML models, ensuring the semantic layer is enriched with accurate labels, descriptions, synonyms, and business logic that AI tools — including Looker Conversational Analytics and external LLMs — can reliably consume; • Design and configure domain-focused Data Agents with well-structured instructions, Golden Queries, and scoped context governance to deliver accurate and trustworthy conversational analytics; • Develop and maintain CI/CD pipelines that automate metadata governance, including AI-assisted description generation, DDL synchronization, and semantic consistency checks; • Build reusable AI skills, tools, and workflows that allow analysts and stakeholders to interact with governed data through natural language interfaces; • Partner with Data Analysts, Data Engineers, and business stakeholders to translate vague or poorly defined business questions into well-defined, measurable data models and agent instructions; • Identify opportunities to improve the quality and reliability of AI-generated insights by improving context structure, metadata coverage, and semantic layer precision; • Develop recommendations and drive improvements to data standards, processes, and definitions; • Contribute to cross-team guidelines and best practices for responsible AI usage with data.