• Own and execute a rolling one-year Data & Analytics roadmap, balancing urgent business needs with foundational work. Guide Vahid through clear priorities, design reviews, code review, and reusable modeling patterns.
• Lead discovery across Marketing, Sales, Customer Success, Product, and Finance to uncover the decision behind a request and translate it into agreed metric definitions, governed semantic layers, and data models that connect the full customer lifecycle.
• Set the analytical architecture and dbt standards; design, build, test, and document the most critical governed models yourself using SQL and dbt.
• Own the full-funnel measurement framework—from marketing reach, demand generation, attribution, and customer acquisition cost through lead and pipeline conversion, sales cycle, bookings, and revenue to onboarding, product adoption, expansion, renewal, churn, and customer lifetime value. Keep core recurring-revenue definitions finance-grade and reconciled while showing how upstream activity drives downstream customer outcomes.
• Own the logic and quality behind executive dashboards and self-service analytics across Power BI, Metabase, and AI-enabled interfaces. Present the results to senior leaders, explain every assumption, and respond constructively to challenge.
• Partner closely with Vahid on warehouse and pipeline requirements, resolve conflicting numbers at the source, and raise the technical quality of the team. Apply AI where it improves development speed or data access, while using sound judgment around privacy, governance, validation, and trust.

A Typical Week

This is a hands-on lead role. The exact balance will shift with business priorities, but you should expect roughly half your time to remain close to the data—building, reviewing, and investigating—and the rest to be split across discovery, functional leadership, and decision support.

• Design or improve a dbt model that connects data across HubSpot, our LMS, billing, or other source systems; write the SQL, tests, documentation, and metric logic needed to put it into production.
• Run discovery sessions with leaders in Marketing, Sales, Customer Success, Product, or Finance to clarify the decision they need to make, challenge the requested metric, and agree on the definition.
• Work side by side with Vahid on source-data requirements, architecture choices, and implementation; review code and unblock delivery without handing off all of the technical work.
• Investigate a commercial or customer-lifecycle question, improve an executive or functional dashboard, and present the conclusion—including assumptions and limitations—to the relevant leadership team.
• Reprioritize the Data & Analytics roadmap, resolve competing requests, and invest in the semantic layer, governance, and reusable modeling foundations that reduce future ad hoc work.

What Success Looks Like

Within your first three months, you have established a clear operating model with Vahid, mapped ownership of the most important metrics, agreed the Data & Analytics roadmap, and resolved at least one material conflicting-number problem.

Within six months, AIHR has a governed customer-lifecycle domain in dbt and an executive view connecting acquisition and pipeline to revenue, retention, and product usage, and has launched the first MVP of our agentic analytics platform to drive self-serve adoption. The underlying definitions are documented, reconciled where relevant, and trusted across Marketing, Sales, Customer Success, Product, and Finance.

Within twelve months, two or three priority business domains are governed end to end, leadership can trace performance from acquisition through lifetime value using consistent numbers, self-service adoption is growing, and Vahid can independently implement the modeling patterns and quality standards you established.

Throughout, you remain hands-on in SQL, dbt, Snowflake, and analytics while setting direction, coaching through the work, and earning leadership's trust in both the numbers and the roadmap.

We offer

• A high-impact role as the functional lead of a small Data & Analytics team, with end-to-end ownership of the semantic layer behind company-wide decisions.
• Competitive, benchmarked compensation.
• Substantial autonomy to shape your day-to-day work, with regular growth conversations and support for your career goals.
• Flexible working hours and work-from-home arrangements after onboarding.
• 26 paid holidays per year, plus one extra day for each of your first five years at AIHR.
• The option to exchange any two public holidays for two days of your choice.
• A work-from-anywhere policy that lets you work abroad for up to 20 days per calendar year.
• A pension plan, four Trust Days for ad hoc recovery, and access to the OpenUp mental health platform.
• A personal development budget, training opportunities, and the books you need to keep learning.
• All public-transport expenses covered through an NS Business Card, plus a MacBook and the equipment you need.
• A beautiful office opposite Rotterdam Central Station, a satellite office in Amsterdam, daily chef-prepared lunch, regular drinks, team activities, and free access to the office gym.

Get a taste of working at AIHR in our AIHR Benefits Guide .

Apply for the Job

Are you excited to become our Lead Analytics Engineer? We would love to hear from you. We are aiming for a start date in December 2026 or January 2027.

P.S. If this role excites you but you do not meet every point, we still encourage you to apply. We care about the evidence behind your experience and your potential to grow—not a perfect checklist.

You are a hands-on technical leader: commercially sharp, calm under challenge, and confident enough to defend your numbers in any room. You also have the patience to build data foundations properly and raise the quality of the work around you.

• You bring 5+ years of experience across analytics engineering, BI, data engineering, or business-facing analytics roles, including substantial recent hands-on analytics engineering and evidence of leading work beyond your own delivery.
• You have a proven track record of building semantic layers or governed dimensional models end-to-end—from business discovery and metric definition to production models and adoption—not only dashboards.
• You have expert SQL and data modeling skills, strong production experience with dbt, and hands-on experience with Snowflake or a comparable warehouse, along with a BI tool such as Power BI, Looker, Omni, or Metabase.
• You bring strong financial acumen, but your analytical experience extends across the full customer lifecycle. You can connect marketing acquisition and attribution, sales pipeline and conversion, revenue and unit economics, customer retention and expansion, and product adoption into one coherent model. You understand how bookings, billings, recognized revenue, cash, ARR, and NRR differ without treating financial reporting as the whole analytical problem.
• You have worked in a B2B SaaS, subscription, fintech, or marketplace business and have modeled several stages of the funnel spanning marketing or growth, sales, customer success and retention, and product usage—rather than specializing in only one function.
• You explain complex logic in plain language, push back constructively on senior stakeholders, and build trust without becoming defensive—even when you discover that your own number or assumption needs to change.
• You take pride in driving the analytical roadmap and architectural vision, leading technical work through architecture and code review, and mentoring experienced colleagues without relying on formal authority. You want the majority of your role to remain hands-on.
• You balance pragmatic execution with rigorous analysis, adapting precision levels to fit business needs and operational constraints.
• You are used to leveraging a broad range of AI tools to boost your productivity and know how to configure them to produce output that lives up to our quality standards and best practices

• Earlier experience in RevOps, Growth, FP&A, product analytics, or commercial analytics, ideally combined with experience in a fast-growing scale-up.
• Experience with HubSpot, marketing automation and attribution data, subscription or billing data, LMS or product-usage event data, Personio, or building semantic layers for AI-enabled and conversational analytics.

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