Data Engineer

Data Engineer

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.

Context & Impact

You’ll join the Data & Analytics team and work in one of two focus areas: 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.

Key Responsibilities

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.
• 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.
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