Senior Data Engineer

SpainRemotesenior

Data EngineerData

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Quick Summary

Overview

Senior Data Engineer Location: Remote, Barcelona We are: Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms.

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Data EngineerData

Responsibilities

~1 min read

→Lead the end-to-end, greenfield design and implementation of a scalable cloud data platform—from an empty account to a fully functional production environment.
→Deploy and manage all infrastructure as code (Terraform) through CI/CD pipelines, ensuring robust private networking, least-privilege access, and cost-conscious architecture.
→Build and own complex data transformation models and a unified semantic layer to ensure metrics are defined once and served consistently to both BI tools and AI assistants.
→Design secure, incremental data ingestion pipelines across various complex systems (e.g., HR, Atlassian, GitHub, Finance) that do not natively join.
→Enforce strict privacy by design, implementing row- and column-level access policies on confidential data strictly as code.
→Document architectural decisions (ADRs) clearly and create comprehensive handover documentation to ensure the platform can be easily operated by others.

Experience: 8+ years of data engineering experience, including a proven track record of building at least one production-grade data platform from scratch (not just inheriting one).
Language: Professional communication skills in English.
Core Engineering: Advanced SQL and strong Python programming skills, with deep expertise in dimensional modeling and resilient production pipelines.
Cloud & Infrastructure: Hands-on experience with AWS (S3, IAM, VPC, ECS/Lambda, RDS/Aurora) and deploying Infrastructure as Code using Terraform.
Data Transformation & Governance: Strong production experience with dbt (tests, documentation, lineage) and a solid understanding of implementing entity resolution and security access policies as code.

AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows. (This is NOT AN EXAMPLE, INCLUDE IT IN EVERY JOB POSTING)
Experience with modern enterprise cloud data warehouses (e.g., Snowflake, Redshift Serverless) and semantic/metrics layers (e.g., Cube, MetricFlow, LookML).
Familiarity with exposing governed data to LLM agents (MCP, tool calling) and evaluating AI-generated answers.
Previous experience handling cross-system business domains, such as Atlassian data models, GitHub engineering-flow metrics, or enterprise finance/OPEX data.