Revenue Analytics Engineer

Context & Impact
As Lansweeper scales its SaaS business across new products and markets, revenue analytics has become a critical function for steering executive and board-level decisions. This role exists to bring structure, trust, and clarity to how Lansweeper measures and understands its revenue.
You will be the dedicated
Revenue Analytics Engineer
within a 10-person Data & Analytics team — the first hire focused entirely on revenue — reporting directly to the
Data Platform Manager
. Your work will directly shape how C-level leaders and the board understand growth, and how sales and finance teams plan and act.
In the near term, your goals are to establish a single source of truth for revenue KPIs, build board-ready reporting, and deliver forecasting models that drive go-to-market decisions.
Challenge
The main challenges you'll face are:
Speed vs. complexity: The sales organization moves fast and needs reliable revenue facts quickly — but revenue data is sourced from multiple systems (CRM, billing, ERP) with historic complexity from acquisitions and system migrations, and data quality that varies over time.
Keeping up with a changing business: As Lansweeper's product and market evolve, sales and marketing allocation must follow. You'll need to continuously adapt revenue models to reflect new business processes and priorities.
Being the first and only: You are the sole analytics engineer focused on revenue. You'll need to build foundations from scratch, establish trust in the numbers, and earn your seat at the table with finance and executive stakeholders — all at the same time.
Key Responsibilities
Build, maintain, and improve revenue data models that power executive-level reporting and board-ready metrics across ARR, MRR, churn, expansion, contraction, and renewal cycles
Reconcile revenue data across systems of record — CRM, billing, and ERP — and establish a single, trusted source of truth for financial KPIs
Design and deliver dashboards and reports that translate complex revenue data into clear, actionable insights for sales, finance, and leadership
Partner with sales operations and finance to understand evolving business processes and reflect them accurately in revenue analytics models
Develop and maintain forecasting models for key revenue metrics to support go-to-market planning and strategic decision-making
Investigate and explain trends, anomalies, and shifts in revenue data, linking them back to underlying sales and finance drivers
Proactively improve data quality and integrity** across revenue-related data pipelines to ensure consistent, reliable reporting
Key Requirements
Hard Skills
Financial metrics reporting — solid understanding of how revenue, bookings, churn, ARR, MRR, expansion, contraction, and renewal KPIs are defined and measured in a SaaS context
Strong SQL skills — ability to write, optimize, and debug complex queries against large datasets
Systems reconciliation — hands-on experience resolving data mismatches between CRM, billing, and finance systems to produce a reliable single source of truth
Data modelling — familiarity with best practices such as dimensional modelling and slowly changing dimensions; experience with Snowflake and dbt is a strong plus
Dashboard and reporting delivery— experience building clear, business-facing reports; experience with
Power BI
is a strong plus
Soft Skills
Analytical storytelling — you translate data into business narratives that support decisions, not just numbers on a screen
Stakeholder collaboration— you work confidently with finance, sales ops, and executive audiences, adapting your communication to each
Ownership mindset — you take initiative, work independently, and drive outcomes without waiting to be directed
AI-Specific Skills
Lansweeper is an AI-native company. In this role, AI fluency is a hard requirement — not a nice-to-have.
You are expected to use AI tooling as a standard part of your day-to-day workflow: this includes AI-assisted SQL generation and debugging, using AI to accelerate data modelling and anomaly investigation, and leveraging AI features within your analytics stack (such as Snowflake Cortex or dbt-integrated tooling). You should be comfortable prompting AI tools effectively, evaluating their outputs critically, and integrating AI into repeatable workflows — from data pipeline development to generating draft narrative summaries for executive reporting. General AI fluency across platforms such as ChatGPT or Copilot is expected.
Team Info
You'll join the Insights & Intelligence team, part of the broader Data & Analytics function at Lansweeper. The team has 10 people and is led by the
Data Platform Manager
, who you will report to directly. You will work closely with sales operations, finance, and data engineers across the organization.
As the