Data Scientist

Technical requirements

Essential requirements

• More than 5 years of experience in Data Engineering or Analytics Engineering.

• Advanced SQL.

• Solid experience in:

• Data Warehouse / Lakehouse

• Dimensional modeling

• ETL / ELT pipelines

Desirable

• Power BI (semantic model and datasets, not just visualization).

• Cloud stack (preferably Azure).

• Python, Spark, dbt, or equivalent tools.

• Experience preparing data for ML / AI use cases.

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Design, lead, and evolve corporate data architecture, ensuring quality, scalability, and readiness for advanced analytics and AI. Act as the technical reference for the Data & BI area and work closely with the Data & BI lead, who owns the business vision and prioritization.

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Main responsibilities

Data Engineering and Architecture

• Design and maintain end-to-end data architecture (sources, ingestion, transformation, consumption).

• Define data models oriented to analytical consumption and data products, not just reporting.

• Evolve the Data Warehouse / Lakehouse toward a scalable and maintainable model.

• Establish modeling standards (dimensional, semantic layer, certified datasets).

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Automate data pipelines

• Design automated, robust, and observable data pipelines.

• Eliminate manual processes, ad-hoc scripts, and fragile dependencies.

• Implement error control, automatic retries, and alerting.

• Guarantee SLAs for data freshness, availability, and quality.

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Data quality and governance

• Define and implement data quality rules with automatic validations.

• Ensure traceability, lineage, and consistency of data.

• Collaborate on defining Data Contracts and standards for datasets.

• Actively participate in data governance alongside the Data & BI lead.

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Readiness for advanced analytics and AI

• Prepare datasets suitable for analytical models and machine learning (historical, granularity, features).

• Lead pilots for forecasting, scoring, or other predictive models of value.

• Establish the foundations of MLOps: versioning, reproducibility, and deployment to production.

• Facilitate the integration of analytical results into business processes.

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Technical leadership

Act as the technical reference for the Data team

• Raise the team's technical level through best practices and code reviews.

• Make technical decisions on the "how" (architecture, tools, patterns), aligned with the area's objectives.

• Collaborate in a structured way with BI, business stakeholders, and IT.

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• Product mindset, not just project-based.

• Ability to structure, prioritize, and simplify.

• Technical rigor with a pragmatic approach.

• Ability to identify and anticipate scalability issues.

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• Data stability and reliability.

• Reduction of manual incidents and rework.

• Improved time-to-data.

• Team capacity to evolve beyond reporting.

• Technical foundation ready for data products and advanced analytics.