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.

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

The main challenges you'll face are:

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

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

Key Requirements

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