Data Engineer

• Implicarse y liderar conversaciones, modelos y decisiones que estructuran los datos de una empresa global, transformando procesos de negocio complejos en modelos de información claros, reutilizables y alineados con la estrategia de datos.
• Mantener y evolucionar el Enterprise Data Model y el Data Domain Map de la organización.
• Definir conceptos, taxonomías y estándares semánticos que todos puedan entender y aplicar.
• Liderar reuniones con stakeholders de negocio para entender cómo funciona el negocio y modelarlo en estructuras de información consistentes.
• Colaborar con equipos de datos, arquitectura de soluciones e ingeniería para asegurar la correcta implementación de los modelos en entornos basados en Google Cloud y herramientas de analítica como Power BI.
• Definir e impulsar buenas prácticas de modelado en toda la organización, fomentando la adopción de modelos compartidos entre diferentes dominios de negocio.
• Garantizar la alineación entre arquitectura de información, gobierno del dato y calidad de datos.
• Participar en la mejora continua de herramientas como catálogos de datos y sistemas de gestión de metadatos.

Data Architect – Semantic Modeller

• Own data from source to delivery. Design ingestion from source systems into BigQuery with Airflow (Cloud Composer), Dataflow, and Python loaders; model it in dbt; and deliver the analysis in Hex. No handoffs, no "that's not my layer."
• Build for 10x. We're growing fast, and the data is growing with us. Every pipeline and model you ship should survive the volume multiplying in the next few months. Reliability, cost, and partition strategy are part of the design, not an afterthought.
• Explain what's driving the numbers. When a metric moves, you find out why. You can trace a number from a Hex dashboard back through the dbt models to the raw source and tell the business what actually pushed it up or down.
• Work AI-first, and own the output. You lean hard on modern AI tooling (coding agents, LLMs) to move faster across the whole stack, but you stay accountable for what lands in production. You know the difference between letting AI draft and letting AI decide, and you keep correctness, cost, and ownership in your own hands. This matters a lot to us.
• Partner directly with business teams. You don't wait for tickets. You understand what growth, finance, and product are trying to learn, get ahead of it, and translate messy questions into models and answers they can trust.
• Manage infrastructure as code. Provision and evolve the data platform with Pulumi, and treat the pipeline as a production system you're on the hook for.
• Stay on the frontier. You actively track what's new in data engineering and AI, and you bring the good tools in instead of waiting for someone to tell you about them.

Senior Data Engineer

• Improve code base and data model of the data estate
• Assist with maintenance tasks and daily data operations
• Monitor data loading process for Datawarehouse ETL/ELT
• Migrate Central Backend to Central repository
• Collaborate on migration of architecture to Microsoft Fabric
• Involve in web scripting data analytics project
• Enhance data pipeline and auditing processes
• Support production issues across pipelines
• Conduct ad hoc data analysis for business stakeholders

• Data modeling & transformation
• Build and maintain DBT models in Snowflake — design layered architecture (staging, intermediate, marts), write data quality tests (Elementary)
• Write clean, well-structured Python and SQL; participate in code reviews and help define engineering standards for the data team
• Design and maintain data pipelines using Airflow (DAGs in Python)
• Manage data integrations via Airbyte — starting with marketing sources (GA4, Facebook Ads, TikTok Ads) and expanding to other streams over time
• Use AWS S3 as the primary storage layer — manage buckets, partitioning, and access control
• Own CI/CD pipelines for data workflows — automated testing, deployment, and monitoring
• Monitor data quality and pipeline reliability; proactively identify and fix issues before they reach downstream consumers
• Work closely with analysts to understand their data needs and translate them into well-modeled datasets
• Bridge the gap between raw data and analyst-ready tables — create models clearly so analysts can work independently

• Designing and developing scalable and efficient data pipelines, ETL processes, and data integration solutions.
• Ensuring data quality and reliability by implementing data validation, cleansing, and quality monitoring processes.
• Optimising database performance by tuning queries, implementing indexing strategies, and analyzing performance metrics.
• Collaborating with cross-functional teams to gather requirements, understand data needs, and develop solutions.
• Staying up-to-date with emerging technologies and industry trends in data engineering.
• Establishing and enforcing best practices and standards for data engineering.

• Lead the design and evolution of our core data architecture across transactional, analytical, and long-term storage systems.
• Own and improve PostgreSQL data design, schema structure, indexing strategy, query performance, relationships, constraints, and data integrity.
• Design, review, and maintain ERDs and data models that clearly represent business logic, system behavior, and domain relationships.
• Work closely with backend engineers to ensure application data models are scalable, consistent, and suitable for both operational and analytical use cases.
• Build and improve data pipelines from production systems into analytics databases and reporting layers.
• Design reliable approaches for data ingestion, replication, transformation, validation, rollups, aggregations, and historical storage.
• Help define the right architecture for hot, warm, and cold data storage.
• Support the design of analytics-friendly datasets for reporting, dashboards, product analytics, finance, compliance, and operational monitoring.
• Help evolve our analytics database strategy, including how we use analytical stores, materialised views, denormalised tables, and aggregation layers.
• Establish data modeling best practices across the engineering team.
• Identify and fix data quality issues, including inconsistent schemas, duplicated logic, missing relationships, poor naming, and unreliable reporting sources.
• Collaborate with backend, product, DevOps, and leadership teams to turn business requirements into clean, scalable data structures.
• Define best practices for migrations, data retention, archival, backfills, and safe deletion of production data.
• Mentor engineers on database design, ERDs, data modeling, and data pipeline thinking.

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