Analytics Engineer at Trendyol

Team: Data Management

About the Team

At Trendyol Tech, our mission is to create a positive impact in our ecosystem by enabling commerce through technology.

We solve complex problems with data, creativity, and agility — always driven by real outcomes. With a culture built on learning, collaboration, and ownership, we grow together while building what’s next.

About the Role

As an Analytics Engineer, you will act as the bridge between raw data and business insights. You'll create the systems that empower our data teams, building pipelines and data models that transform messy, siloed data into clean, accessible, and reliable datasets. You will play a key role in how we make informed decisions by providing the single source of truth that powers our dashboards, reports, and analytical tools.

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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