Data & Analytics Engineer

• Knows as much about data as about business.

• Writes SQL, works with modern analytics stack tools, and has the initiative to identify which processes can be automated or improved before anyone asks.

• The technological stack is already in place:

BigQuery as data warehouse

Fivetran for ingestion

dbt for transformations,

Looker for dashboards

N8N for automations.

• Person with 3-5 years of experience in similar roles.

• Advanced SQL: windows, CTEs, query optimization in large-scale data warehouse environments.

• dbt: layered modeling, macros, tests, documentation, materializations, incremental models.

• Jinja and YAML as standard working languages within dbt projects.

• BigQuery: table design, cost optimization, partitioning, clustering, and basic permission management.

• Python at a scripting level and data process automation.

• Git: working with branches, pull requests, and CI/CD pipelines. The stack is already in Git with CI/CD - you are expected to use it from day one.

• Basic GCP: IAM, Cloud Storage, Cloud Scheduler, and Cloud Run as a dbt orchestrator.

• Looker or equivalent BI tool: construction of dashboards and semantic layer for self-service.

• Star schema / Kimball methodology for dimensional model design.

• Regular use of AI tools (Claude, Gemini, Copilot, or others) as part of the workflow, not just as occasional curiosity.

• Ability to translate business needs into data specifications without intermediaries.

• Self-service mindset: building so that others can use it without constantly asking for help.

• Ability to communicate technical concepts to non-technical profiles in a clear and actionable way.

• Proactive attitude.

• Eagerness to learn and grow technically.

• VALUABLE

• Experience with Fivetran or other ELT/CDC tools for data ingestion from SaaS sources.

• Knowledge of LookML for Looker's advanced semantic layer.

• Experience with N8N or other low-code automation tools.

• Airflow: reading and maintaining existing DAGs in Python.

• Experience with data observability and quality tools (Elementary - open source for dbt, Great Expectations, Monte Carlo).

• Knowledge of internal tools for the development of the internal operations portal (AppSheet, Retool,...).

• Experience migrating legacy pipelines (Pentaho, ad hoc Python scripts, Airflow) to modern stacks.

• Machine Learning / predictive models: experience with BigQuery ML, scikit-learn, or similar frameworks for predictive analysis on internal data.

• AI applied to data: building flows that combine internal data with language models (chatbots over data, automatic generation of narrative reports, anomaly detection with AI).

• Experience with more advanced data catalog tools: DataHub, Alation, or similar.

At Libelium, we develop innovative technological solutions based on environmental data through digital twins, enriched with local information captured by our IoT devices. Our technology allows smart cities to optimize their low-emission zones and critical infrastructures to improve their efficiency and security through advanced environmental monitoring, as in the case of electric power transport.

Our IoT devices connect the physical world with the digital one through our space ♾️iris360, driving sustainability, productivity, and datacratization in companies and cities across more than 120 countries.

iris360 is Libelium's core platform: a multi-tenant SaaS/On-premise ecosystem for IoT data management, Digital Twins, and European Data Spaces. It is deployed in hybrid environments (OVH Cloud + AWS) and in on-premise installations in more than 50 cities, with clients ranging from public administrations (Asturias DXC, Cartagena, European municipalities) to international organizations (UNDP, UNECE).

The platform grows in modules (envair360, grid360) and each new project involves a replicated deployment of the base infrastructure. Currently, we are looking to strengthen the team. We firmly believe that data is the new language of communication between administrations, companies, and citizens, facilitating the creation of a positive impact on the planet.

• Our 🎯 MISSION: We transform data from companies and cities into decisions that make our planet more sustainable.

• Our 👁️ VISION: To build a resilient, connected, and data-driven future, collaborating with change makers who share our values.

• Our 💎 VALUES: We connect reality with knowledge, drive decisions with precision, and transform data into sustainable impact.

At Libelium, we are looking for a Data & Analytics Engineer with 3-5 years of professional experience in a similar role.

You will be part of the Digital & Data area, the company's technological engine:

• Infrastructure

• Automation

• Data and AI at the service of the business.

We work with a philosophy of continuous improvement and rapid iteration, and we are looking for someone eager to contribute a modern vision of infrastructure within a small team with direct impact.

• You will be responsible for updating and maintaining the company's data layer: from transformations in our data warehouse to the dashboards and reports that teams use to make operational and strategic decisions.

• Additionally, you will have the role of enabling self-service: building the data layer and the necessary training so that company teams can generate their own reports and visualizations without depending on this profile for every query.

• Design, build, and maintain data models in dbt on BigQuery following layered architecture (staging, intermediate, marts) and star schema / Kimball methodology.

• Implement incremental models in dbt for high-volume tables, reducing execution costs in BigQuery.

• Maintain documented data lineage in dbt, so that any change in a source can be traced to the dashboards it impacts.

• Translate business needs from internal teams (sales, operations, finance) into actionable data specifications and reusable models.

• Guarantee the quality, consistency, and traceability of corporate data: define and maintain quality tests (not_null, unique, accepted_values, relationships) in dbt.

• Standardize data in the staging layer: names, formats, enums, null and duplicate fields, so that downstream models do not inherit source problems.

• Maintain an active data catalog so that any team knows what data exists, what it means, who maintains it, and when it was last updated.

• Continuously monitor data quality and act on anomalies before they impact teams or management reports.

• Develop and maintain dashboards and reports in Looker for different areas of the company, oriented towards operational and strategic decision-making.

• Build the semantic layer in Looker that allows non-technical teams to explore data, generate their own visualizations, and export reports without needing help from the data team for every query.

• Train internal users in the use of Looker: how to filter, explore, create visualizations, and read data autonomously.

• Perform ad hoc analysis when management needs to answer questions that are not in any existing dashboard.

• Build and maintain automation flows with N8N that streamline the company's day-to-day processes.

• Develop and maintain internal mini-applications (forms, interfaces, process status pages) that connect data with automation flows, as part of the internal operations portal.

• Participate in the evaluation and possible evolution of the current internal portal towards a more robust and maintainable solution.

• Manage and monitor dbt executions in Cloud Run: scheduling, execution order, failure alerts, and notifications.

• Basic management of the GCP environment where the data stack lives: IAM permissions, Cloud Storage, Cloud Scheduler.

• Maintain existing Airflow DAGs and migrate legacy developments to the new infrastructure, documenting the business logic in the process.

• Document all developments in Git with clear commit messages, code reviews, and deployments through the existing CI/CD pipeline.