Your mission?

Turn our rich operational and product telemetry into trustworthy, timely, secure, and business-ready data. Build the semantic layer and pipelines that power GTM, Product, Operations, and Partner Success-so decisions are faster, experiments are rigorous, and growth is compounding.

The role

What are we looking for?
We're hiring a Principal Data Scientist / Developer-part architect, part builder, part strategist-to own our end-to-end data foundation and accelerate impact from insights to shipped improvements.

You'll partner with Product and Engineering leads to design the next-gen data platform (batch and streaming), define metrics and contracts, and ship models (analytics, ML, and LLM-assisted) that lift activation, expansion, and reliability.

Reporting to the COO, you'll work hands-on while coaching others, translating business questions into robust, production-grade data products. This role is based in Madrid, Spain, with a hybrid work model.

This isn't about dashboards alone-it's about building the engines that move the business.

What you'll own and drive

• End-to-end data infrastructure: From ingestion to orchestration to serving (batch/stream), optimizing cost, reliability, and speed.

• Data quality, integrity, and availability: CI/CD for data, contracts, tests, and observability as first-class citizens.

• Pipelines & processes: Design/optimize ingestion, transformation, and analysis workflows for structured and unstructured data.

• Insights to action: Partner with GTM, Product, Operations, and Partner Success to prioritize questions, run experiments, and land changes.

• Models & semantics: Own core metrics, semantic layers, reporting models, and feature stores aligned to the partner funnel (SQL→MQL→SQL→ARR).

• Governance & collaboration: Lineage, catalog, permissions, and clear documentation so teams can self-serve safely and confidently.

Day-to-day tasks

• Design/optimize pipelines (ingestion, transformation, orchestration).

• Develop and maintain analytical and ML/LLM models (structured/unstructured; stats/experimentation).

• Implement/improve dashboards (Dash/Plotly, Power BI, Tableau, Looker).

• Analyze trends/patterns to improve processes, product, and the partner funnel.

• Data modeling (dimensional, lakehouse, feature stores).

• Data quality & observability (detect/fix inconsistencies).

• Coordinate accessibility & usability (catalog, lineage, permissions).

• Automate extraction/transformations, data testing, and deployment.

• Document best practices, standards, and methodologies.

Tooling you'll use
Linux, bash, Git, Containerization; SQL/NoSQL; Flask/FastAPI for APIs; Data stack: Pandas, scikit-learn, Polars, DuckDB; DL stack: PyTorch, HuggingFace, TensorFlow; Dash/Plotly/Power BI; Slack; Microsoft O365; Jira; Confluence; Cloud platforms & data services.

What will make us fall in love with you as a candidate?

It will be a perfect match if...

• You are proactive. You spot data gaps early and propose scalable solutions without waiting for direction.

• You prioritize clarity over clutter. You define crisp metrics and contracts, and make complex systems understandable.

• You rock in strategic storytelling. You turn analysis into compelling narratives that move execs and engineers to act.

• You are brave & creative. You experiment fast, embrace feedback, and solve problems others deem "too technical" or "too messy."

• You simplify the complex. You design data models and APIs that make cloud workflows intuitive for partners and internal teams.

• Ownership. You can lead the full data lifecycle-from ingestion to models to serving and observability-hands-on and end-to-end.

• You love being the bridge between tech and business. You translate questions into analysis, and insights into shipped changes.

• For you, user and business impact go first. You champion data integrity and responsible AI to improve real outcomes.

• Trust. You give and receive feedback thoughtfully, with the intent to build, and you lead with transparency.

Requirements that are important for us

Please, only apply if you match these.

• A principal level: 8+ years across Data Science/Analytics/Engineering (including leading initiatives), with shipped data products and measurable business impact.

• Mastery of data tooling: Python (preferred) and/or R; SQL & NoSQL; visualization (Power BI, Tableau, Looker, Dash/Plotly); ML/LLM/stats; cloud data services. You use AI responsibly to speed up quality work.

• English skills: Intermediate level required - Level B1 minimum (B2+ is a plus) to collaborate with international teams.

Data

• SQL & NoSQL excellence: Schema design, performance tuning, partitioning, indexing.

• ETL/ELT & orchestration: Reliable pipelines (batch/stream) with monitoring, alerting, and recovery.

• Modeling: Dimensional modeling, lakehouse patterns, and feature stores that serve analytics and ML.

• ML/LLM & experimentation: From baseline models to rigorous A/B tests and causal inference on structured/unstructured data.

• Visualization: Power BI, Tableau, Looker, and Dash/Plotly for decision-ready views.

• Observability & governance: Data tests, lineage, contracts, catalogs, and CI/CD for data.

• Quality by design: Freshness, completeness, accuracy, and SLAs as non-negotiables.

Engineering

• System design & architecture: Comfortable with monoliths, microservices, and event-driven patterns.

• APIs & services: Build/consume services with Flask/FastAPI; productionize models and data apps.

• Cloud & data services: Practical fluency with cloud storage/compute and managed data offerings.

• Core stack: Python, Pandas, Polars, DuckDB, scikit-learn; DL with PyTorch/TensorFlow/HuggingFace.

• DevX: Linux, bash, Git, containerization; automation, testing, and CI/CD for code and data.

• Security-minded: Design with least privilege, encryption, and secure defaults.

Security & Compliance
The Data Analytics team ensures secure and responsible data use in line with ISO 27001, ENS, and HDS-protecting confidentiality, integrity, and availability.

We anonymize and encrypt where applicable, enforce access controls, uphold data quality, and ensure that analysis tools/platforms are compliant. You'll work closely with our Security team to bake this into architecture and day-to-day operations.