Data Engineer

Data Engineer

Context & Impact

As Lansweeper scales its SaaS business across new products and markets, trusted data has become critical to how we make executive and board-level decisions. Two questions sit at the center: how is our revenue performing and how are customers using our products? We’re hiring two Data Engineers to bring structure, trust, and clarity to both.

Revenue Analytics: Build the foundation for how Lansweeper measures growth: ARR, retention, expansion, and forecasting. Your work will shape how C-level leaders, the board, sales, and finance understand performance and planning.

Product Insights: Build the pipelines and models that show how customers adopt and use our products. Your work will help product teams decide what to build next and help customer success spot risk and opportunity early.

Both roles share one goal: connecting commercial performance with product behavior, so the business can see not just what is happening, but why. In the near term, you’ll help establish a single source of truth for your domain’s KPIs, build reliable, board- and leadership-ready reporting, and enable the forecasting and insight models that drive go-to-market and product decisions.

You don’t need to know our products or industry already. Curiosity, strong engineering fundamentals, and an interest in the business side of data matter more.

6-12 Month Goals

• Develop a strong understanding of the data platform, architecture, and key data flows supporting business reporting and analytics.
• Become a trusted partner for internal stakeholders by delivering reliable, well-structured, and scalable data solutions that support decision-making.
• Improve the quality, consistency, and availability of data by strengthening pipelines, models, and data management practices.
• Collaborate with cross-functional teams to understand business requirements and translate them into robust technical solutions.
• Support the company’s data maturity by enabling better access to trusted data and contributing to a scalable analytics foundation.

Challenges

• Working within a growing organization where data sources, systems, and business processes have evolved over time and may have been customized to meet specific needs.
• Designing scalable and maintainable data solutions in an environment where requirements may change frequently as the business grows.
• Ensuring data quality, consistency, and reliability across multiple systems and pipelines, while managing dependencies and complexity.
• Translating business needs into technical implementations that are both practical and aligned with long-term platform standards.

For both roles:

• Build, maintain, and improve data pipelines and models that power executive-level reporting and board-ready metrics in your focus area
• Establish a single, trusted source of truth for your domain’s KPIs, reconciling data across systems of record
• Build the curated data models and semantic layer that power dashboards and reporting, in partnership with analysts
• Partner with business stakeholders (sales operations, finance, product, customer success) to understand evolving processes and reflect them accurately in our data models
• Investigate data anomalies and trace them back to their source (pipeline issue, upstream system change, or genuine business shift), then work with stakeholders to resolve them
• Proactively improve data quality and integrity across your pipelines to ensure consistent, reliable reporting
• Month-end close support: Reporting is time-critical at month-end. When the end of the month falls on a weekend, you’ll be part of a rotation to monitor the pipelines and make sure the numbers are ready for finance and leadership on the next business day. This is occasional and planned in advance, and the load is shared across the team.

Revenue Analytics track:

• Own revenue data models covering ARR, MRR, churn, expansion, contraction, and renewal cycles
• Reconcile revenue data across CRM, billing, and ERP to produce one consistent view of financial KPIs
• Build and maintain the data foundation for forecasting models, working with analysts to support go-to-market planning
• Work closely with sales and finance operations to keep revenue models aligned with how the business actually sells and bills

Product Insights track:

• Own product usage and adoption data models covering active usage, feature adoption, engagement, and customer health
• Integrate product telemetry with commercial data (accounts, subscriptions, renewals) so usage can be tied to retention and expansion
• Define and maintain product KPIs together with product management and customer success
• Build the foundations of the self-serve dashboards and insight models that help product teams prioritize and help customer success spot risk and opportunity early

For both roles:

• Hard skills:
• Strong SQL: you can write, optimize, and debug complex queries against large datasets
• Data modelling: familiarity with best practices such as dimensional modelling and slowly changing dimensions
• Pipeline engineering: experience building and maintaining reliable, tested data pipelines, including monitoring and data quality checks
• Supporting reporting: experience preparing data models that feed business-facing dashboards and reports
• Strong plus: experience with Snowflake, dbt, Fivetran
• Soft skills:
• Business understanding: you take the time to understand how the business works and why the numbers matter, and you can explain technical decisions and data issues in plain language to non-technical colleagues
• Stakeholder collaboration: you work well with analysts, finance, sales ops, product, and customer success, adapting your communication to each
• Ownership mindset: you take initiative, work independently, and drive outcomes without waiting to be directed
• Reliability: you’re comfortable owning time-critical deliverables, including occasional month-end weekend monitoring as part of a team rotation

Revenue Analytics track:

• SaaS financial metrics: solid understanding of how revenue, bookings, churn, ARR, MRR, expansion, contraction, and renewal KPIs are defined and measured
• Systems reconciliation: hands-on experience resolving data mismatches between CRM, billing, and ERP to produce a reliable single source of truth
• Nice to have: experience with Salesforce and NetSuite data, or with forecasting models

Product Insights track:

• Product and usage data: experience modelling event, telemetry, or usage data, such as active usage, feature adoption, and engagement
• Linking usage to business outcomes: understanding of how product behavior connects to retention, expansion, and customer health
• Nice to have: experience with a product analytics tool or with IT asset, security, or infrastructure data

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 tech stack. You should be comfortable prompting AI tools effectively, evaluating their outputs critically, and integrating AI into repeatable workflows — from data pipeline development to drafting documentation, data tests, and lineage descriptions. General AI fluency is expected.

Team Info

You’ll join the Data & Analytics team, part of the broader Insights & Intelligence function at Lansweeper. Within the data team, you’ll collaborate daily with data analysts, data architects and fellow data engineers, so the models you build fit into a platform that is consistent and well governed.

Outside the data team, your main stakeholders will depend on your track. Revenue Analytics works closely with sales operations and finance, while Product Insights works with product management and customer success. Either way, you’ll be the link between those business teams and our data platform.

You’ll be one of two engineers joining to build out these domains, with real ownership of your area end-to-end. It’s a rare opportunity to shape how a growing SaaS company understands and steers both its revenue and its product.

The team is based across Ghent, Belgium and Alicante, Spain, with a hybrid working model at both locations.

• Meal vouchers
• Eco vouchers
• Hospitalisation insurance
• Group insurance
• Company car or mobility budget
• Phone plan
• Internet allowance
• Health insurance
• Meal allowance
• Flexible remuneration plan
• A high-impact, high-autonomy role at the intersection of data, finance, product, and go-to-market strategy
• Hybrid working from either Ghent, Belgium or Alicante, Spain — flexibility built in by design
• A pragmatic, collaborative data team that values ownership and real-world impact over process for process's sake
• The opportunity to build the revenue and product analytics foundation of a scaling SaaS company
• A front-row seat to how leadership uses data to steer the business, with your work directly influencing strategic direction

Lansweeper is the AI Cyber Asset Intelligence platform helping IT and Security teams gain full visibility, reduce cyber risk, and scale automation with confidence.

In today's complex IT, OT, cloud, and IoT environments, fragmented asset data slows decisions and increases risk. We transform raw asset data into a continuously validated, trusted source of truth — so teams can move faster and act with certainty.

With Lansweeper, organizations can:

See – Truly complete visibility across hybrid environments

Know – Enriched asset intelligence with lifecycle and risk context

Act – Automate workflows, coordinate remediation, and enforce policy at scale

From universal asset discovery to AI-powered intelligence, we provide the shared foundation modern IT Operations, Cybersecurity, and Digital Transformation teams rely on.

Our culture — built on four core values:

• One Team – United across boundaries
• We Care – Customers and people at the centre
• We Grow – Learning, sharing, improving
• We Deliver – Focusing on what truly matters

Pay Transparency

At Lansweeper, every role is assigned to a level defined by three things: the expertise it requires, the complexity it involves, and the scope of impact it carries. Each level maps to a salary band based on salary benchmarks from the Software/SaaS industry of your country, so your pay reflects what the market pays for comparable roles.

Where you land within that range depends on the expertise you bring, the complexity you have navigated, the scope of impact you have owned, and your track record of delivering results in comparable roles — the same criteria applied consistently to every candidate.

We welcome applicants of all backgrounds, regardless of gender, religion, ethnic origin, age, sexual orientation, or disability.

How AI Is Used During the Hiring Process

We use AI to support and not replace during the hiring process. HR decisions are made by our talent acquisition team. You can request more info at any time via recruitment@lansweeper.com

Diversity Statement

It is Lansweeper's policy to provide equal employment opportunity to all applicants and employees.

Lansweeper disapproves of, and will not tolerate, unlawful discrimination against any applicant or employee because of race, color, national origin or ancestry, gender (including pregnancy, childbirth, or related medical conditions), gender identity, age, religion, disability, family care status, veteran status, marital status, sexual orientation, or any other basis protected by local, state, or federal laws.

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