Data engineer cloud platforms

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.

• 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.

• 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).
• Professional communication skills in English.
• Advanced SQL and strong Python programming skills, with deep expertise in dimensional modeling and resilient production pipelines.
• Hands-on experience with AWS (S3, IAM, VPC, ECS/Lambda, RDS/Aurora) and deploying Infrastructure as Code using Terraform.
• Strong production experience with dbt (tests, documentation, lineage) and a solid understanding of implementing entity resolution and security access policies as code.
• Будет плюсом: 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; 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

• A High-Impact Environment
• Commitment to Professional Development
• Flexible and Collaborative Culture
• Global Opportunities
• Vibrant Community
• Total Rewards *Specific benefits are determined by the employment type and location.