Senior Data Engineer

Wizeline; Barcelona; On-site; EUR 90,000 - 120,000; Full time

Benefits offered by this job

Professional development

Flexible culture

Total rewards

Job summary

Wizeline in Barcelona, Spain, is seeking an senior Data Engineer to lead end-to-end design and implementation of a scalable cloud data platform. You will deploy infrastructure as code with Terraform, build transform models, and ensure secure, governed data pipelines that feed BI tools and AI assistants.

Required: 8+ years in data engineering, strong SQL and Python, AWS and dbt, and fluent English communication.

Qualifications

• 8+ years of data engineering experience, including building a production-grade data platform.
• Strong SQL and Python skills with dimensional modeling expertise.
• Experience with Terraform and AWS, and data governance with dbt.

Responsibilities

• Lead end-to-end design and build of a scalable cloud data platform from scratch.
• Deploy infrastructure as code (Terraform) through CI/CD pipelines and secure networking.
• Create and own data transformation models and a unified semantic layer for BI/AI.
• Design secure, incremental data ingestion across diverse systems (HR, Jira, GitHub, Finance).
• Document architectural decisions and ensure operations handover with ADRs.

Skills

Data engineering

SQL proficiency

Python

Dimensional modeling

Terraform / IaC

AWS

dbt

English communication

Tools

Terraform

CI/CD

AWS services

GitHub

Atlassian tools

Job description

We are:

Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.

With the right people and the right ideas, there’s no limit to what we can achieve.

Are you a fit?

Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:

Key Responsibilities

• 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.

Must-have Skills

• 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.

Nice-to-have:

• 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.

What we offer:

• Commitment to Professional Development
• Flexible and Collaborative Culture
• Total Rewards *Specific benefits are determined by the employment type and location.