Analytics Engineer
Team: Data Management
Location: Istanbul / Maslak
Commitment: Full-time
Workplace Type: hybrid
Responsibilities
- Partner with Product Managers and engineering teams to design, develop, and maintain scalable and reliable data models and metric layers for new and existing data product initiatives.
- Transform large, complex datasets from raw sources into clean, tested, and documented data assets that empower self-service analytics for end-users (analysts, data scientists, business teams).
- Own the end-to-end design, development, and maintenance of SQL-based data models, ensuring consistency, performance, and reliability within the data warehouse.
- Act as the product owner for our analytics platform, driving the standardization of data definitions and metadata to establish a "single source of truth." Additionally, lead the development of our next-generation platform that enables users to conduct self-service analysis through Large Language Models (LLMs) and AI Agents.
- Collaborate closely with business teams to understand their data exploration and analysis needs, influencing data roadmaps and prioritization based on their requirements.
- Deeply understand and empathize with Trendyol's customers and their journey with the product from a data perspective.
Expected Qualifications
- Bachelor's degree or higher in a quantitative discipline such as Computer Science, Mathematics, Engineering, or a related field.
- 3+ years of professional experience in analytics engineering, data warehousing, or data modeling. Experience in an e-commerce environment is a strong plus.
- Strong understanding of modern data warehousing concepts, including fact tables, dimensions, star schemas, and slowly changing dimensions.
- Expert-level proficiency in SQL and hands-on experience with the data modeling layer of modern BI and data visualization tools (e.g., Looker, Tableau).
- Proven hands-on experience in data modeling, data lake technologies, and large-scale data warehousing architecture.
- Experience applying software engineering best practices to data, including version control (Git) and CI/CD.
- Ability to translate complex data concepts and model designs for both technical and non-technical stakeholders, enabling them to tell stories with data.
- An agile mindset for data product development.
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