• Build and maintain marketing attribution systems (UTM tracking, user journey, conversion mapping) • Implement and manage 1st-party data passback to ad platforms (e.g., conversions, revenue, LTV signals) • Configure and maintain tracking infrastructure: - GTM (client + server-side) - Google Ads (Enhanced Conversions) - Meta Pixel + Conversions API - LinkedIn, TikTok, and other ad platforms • Set up and maintain conversion events and schemas across platforms • Ensure consistent event definitions and naming across product, CRM, and ad platforms • Build and manage audience sync pipelines • Integrate CRM and warehouse data into ad platforms for optimization (offline conversions, lead stages, revenue) • Operationalize new marketing experiments (UTMs, tracking parameters, campaign setup) • Debug and resolve tracking and attribution discrepancies across systems (e.g., Ads vs CRM vs warehouse) • Build and validate marketing and pipeline dashboards, ensuring data accuracy and consistency • Implement privacy-safe tracking practices (Consent Mode, CMP integrations) • Partner closely with growth, product, and data teams to ensure measurement supports decision-making

• Own the entire data foundation of a fast-scaling AI company — from raw data to executive metrics. • Build from 0 → 1 — define the architecture that powers product, finance, and company-wide decision making. • High visibility and impact — your work directly informs leadership, product direction, and company strategy. • Help define how data supports AI systems, agents, and long-term intelligence. • Design and build core data pipelines (e.g., product events, payments, internal systems → BigQuery) • Define and maintain the data warehouse architecture, including schema design, data modeling, and table structure • Establish and own the single source of truth (SOT) for product and business metrics • Build and maintain core data models (user, subscription, revenue, engagement, etc.) • Ensure data consistency across systems (product analytics, billing, internal tools) • Lead data reconciliation efforts (e.g., Stripe vs internal systems vs reporting) • Implement data quality checks, validation, and monitoring systems • Build reliable reporting layers used by leadership and finance (not ad hoc dashboards) • Establish data standards and contracts (event naming, schema governance, tracking consistency) • Partner with engineering to improve instrumentation and data correctness at source • Support downstream teams (analytics, DS) by providing clean, well-documented datasets • Continuously improve data reliability, performance, and cost efficiency

• developing and optimizing scalable data pipelines to support critical analytics and business intelligence • designing, building, and maintaining data models and architectures that enable efficient storage, retrieval, and processing of large-scale datasets • working with key stakeholders to define the business problem, determine requirements, and ensure quality deliverables • managing and prioritizing data engineering roadmaps in partnership with business leaders, translating strategic objectives into actionable work items and clear delivery plans • implementing best‑practice data governance, data quality, and data security standards, ensuring that pipelines and datasets are reliable, well‑documented, and compliant with organizational requirements • facilitating regular stakeholder communication, providing updates on progress, risks, and dependencies while ensuring expectations are aligned across technical and non‑technical audiences • conducting code reviews and enforcing data engineering best practices

• Lead the Design and development of scalable data pipelines and infrastructure • Optimize the performance of data systems and troubleshoot complex data issues • Collaborate with data scientists to ensure data is structured and available for advanced analytics • Ensure data security, privacy, and compliance with organizational policies • Manage the integration of new data sources and ensure data quality • Continuously evaluate and adopt new technologies to improve data engineering practices • Contribute to architecture decisions