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. 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: Your work will shape how C-level leaders, the board, sales, and finance understand performance and planning. 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. Curiosity, strong engineering fundamentals, and an interest in the business side of data matter more. 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. Support the company’s data maturity by enabling better access to trusted data and contributing to a scalable analytics foundation. 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. 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 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 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 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 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 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 and usage data: experience modelling event, telemetry, or usage data, such as active usage, feature adoption, and engagement Nice to have: experience with a product analytics tool or with IT asset, security, or infrastructure data 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

📌 Data Analytics Engineer Tableau (Alicante (Alacant))
🏢 Lansweeper
📍 Alicante (Alacant)