• Build and optimize data pipelines and analytical tools for scale • Collaborate with both technical and non-technical areas of the business • Contribute to core data assets for equitable book of business assignments • Build and maintain semantic models for analytics and AI consumption • Build out scalable data models for the HubSpot business • Expand suite of dbt patterns and macros • Support data observability and pipeline reliability • Build analytical applications in Hex • Gather requirements with internal stakeholders • Document data pipelines, processes, and best practices • Contribute to shared analytics best practices across HubSpot’s broader data community

• Build and scale data pipelines for product events, usage data, and customer datasets • Develop analytics infrastructure using technologies like Kafka, ClickHouse, and PostgreSQL • Own data models, KPI definitions, and metric layers powering dashboards and product features • Build reliable ingestion processes for both structured and messy customer-provided datasets • Ensure privacy-first data practices through anonymisation, pseudonymisation, and tenant-level controls • Collaborate with engineering and product teams to make data accessible, trusted, and actionable

Role Description As the Lead Analytics Engineer (Looker), reporting into the Analytics Manager, you will be the primary architect of our data’s "source of truth." You will lead a team of high-performing Analytics Engineers dedicated to transforming raw data into actionable insights through a sophisticated Looker semantic layer. This is a hybrid role requiring both technical mastery and strategic leadership. You will act as the bridge between Data Engineering (infrastructure), Data Analysts, and our Corporate, Marketing, CS, Sales, and GTM Ops Teams (consumers), ensuring our data models are scalable, automated, and governed by rigorous CI/CD practices. Your mission is to eliminate manual toil and empower the organisation with true self-service capabilities. - People Leadership & Development: - Mentorship & Coaching: Lead the AE team, drive a high-performance culture, and support individual career growth. - Performance Management: Drive regular 1-on-1s, give constructive feedback, and handle performance reviews. - Resource Planning: Manage team capacity and sprint priorities, balancing tech debt with stakeholder needs. - Talent Growth: Help with recruitment and make sure new joiners have a smooth onboarding. - Semantic Architecture & Governance: - Own the Layer: Lead the design, development, and maintenance of centralised Looker semantic models (LookML). - Guardianship: Act as the "Gatekeeper" for Looker, enforcing coding standards, modularity, and performance optimisation. - CI/CD Implementation: Establish and manage robust version control and deployment pipelines in GitLab for the semantic layer. - Cross-Functional Collaboration: - Upstream Influence: Partner with Data Engineering to define table structures and schemas that optimize for downstream analytical performance. - Downstream Empowerment: Translate the business needs of Corporate, Marketing, CS, Sales, and GTM Ops Analysts into scalable data models. - Automation & Efficiency: - Scale the Team: Identify manual workflows and automate them using Python scripts, API integrations (Looker API), or Agentic AI. - Operational Excellence: Modernise the team’s workflow by co-creating and enforcing a disciplined Jira ticketing structure to ensure transparency and velocity. Qualifications - Looker Mastery: Expert-level knowledge of Semantic Layer, LookML, Liquid, and Looker administration. Experience with Looker API is a huge plus. Prior experience with migrating from another tool to Looker is highly advantageous. - The Modern Data Stack: Proficiency in SQL (advanced window functions, optimisation) and experience with cloud data warehouses (e.g., Snowflake, BigQuery) as well as at least intermediate knowledge of dbt. - Engineering Mindset: Strong understanding of Git workflows, CI/CD principles, and data modelling methodologies (Kimball, Data Vault, etc.). - Scripting: Ability to write Python to automate workflows or interact with APIs. Prior experience with AI Agents within the conversational analytics space is highly desirable. - Mentorship: Proven experience leading or mentoring a team of engineers in an agile environment. - Communication: The ability to explain complex technical trade-offs to non-technical stakeholders in Marketing or Finance. - Process-Oriented: A passion for documentation, Jira hygiene, and building repeatable processes. Benefits - We're proudly distributed across the globe, with each market being remote-first. Depending on which market you're joining, you might have office access or be fully remote - either works. - Additional benefits that might differ depending on the location. You shall be provided with the benefits details during or after your first conversation with one of our TA Specialists. - Work in a welcoming team which is always ready to help. - Opportunity to develop in an international environment - we have teams in 6 countries.

Role Description We’re looking for a Senior Analytics Engineer to help us build reliable, scalable data products for enterprise customers and internal teams. This is a hands-on role with a strong focus on SQL, dbt, data modeling, data-product development, and practical AI integration—not traditional dashboarding alone. - Own work from a stakeholder question through analysis, modeling, quality assurance, and delivery. - Help strengthen how the team works with dbt and contribute to our shift toward scalable data delivery. - Take on direct customer-facing responsibilities, with support from the team as you build that experience. Qualifications - Expert-level SQL and hands-on, production-level dbt experience, including incremental models, testing, configuration, and performance tuning. - A track record of owning reliable data models or products in production, with strong data-modeling skills and experience working with large, complex datasets. - Strong data-warehouse fluency with BigQuery or an equivalent platform, including the ability to diagnose query cost and performance issues. - Experience using Python for data analysis or data-product development, alongside a practical approach to testing, documentation, data quality, governance, and correctness. - Strong stakeholder-management skills and the judgment to explain technical decisions, manage trade-offs, and move ambiguous work forward. - A direct, pragmatic, and collaborative working style, with concrete examples of using AI to improve how you work independently and with a distributed team. - Excellent communication skills in both written and spoken English. Requirements - Experience with client-facing products, external customers, or complex internal stakeholders in a SaaS or consultancy environment. - Familiarity with tools and practices such as BigQuery, Airflow, Looker or other visualization platforms, cloud analytics platforms, and Agile ways of working. - Experience using AI in recurring workflows, agent-based processes, or customer-facing data products. - Knowledge of travel, hospitality, or short-term-rental data, or experience mentoring colleagues and improving analytics-engineering practices. Benefits - Flexible time off: Autonomy to manage your work-life balance. - Alan Flex benefits: 160€/month for food or nursery. - Flexible retribution: Optional benefits through tax-free payroll deductions for food, transportation and/or nursery. - Wellbeing support: Subsidized ClassPass subscription. - Comprehensive health insurance: 100% Alan coverage for you, your spouse, and dependents. - Impactful work: Shape products relied on by 85,000+ users worldwide. - Referral bonuses: Earn rewards for bringing in new talent.