Top Data Engineer Jobs in Turkey

As a Data Engineer at Zapier, you'll build scalable data systems, enhance product functionality through data, and collaborate with teams to improve data access and usability.

As a Data Engineer, build and manage data pipelines, ensure data quality, and support analytics for trading. Collaborate with teams to define data needs and best practices for infrastructure and applications.

The AI Data Engineer will design scalable data systems, integrate AI/LLM models, and maintain Data APIs to support analysis and applications.

Responsible for developing backend AI features, integrating machine learning models, building data pipelines, and maintaining AI services within a retail ecosystem.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

The role involves scraping, cleaning, and maintaining datasets focused on global wealth migration, producing annual reports, and creating visualizations.

Develop and maintain data pipelines, analyze large datasets, and work with game development teams to enhance game experiences.

As a Data Engineer at Screenverse, you'll build ETL pipelines, ensure data quality, and contribute to data strategy in a collaborative environment.

As a Principal Database Engineer, you will architect scalable PostgreSQL solutions, ensuring data reliability, performance, and collaborating with teams to build a robust data platform for GitLab's growth.

As a Middle Data Engineer, migrate SQL Server data warehouses to Snowflake, refactor ETL processes, build data pipelines, and ensure reliable data movement.

The Backend & Data Engineer will maintain and develop backend features, manage data pipelines, ensure data consistency, and work closely with product and analytics teams.

As a Data Engineer, you'll design, develop, and implement high-performance data infrastructure and pipelines while communicating effectively with various stakeholders.

The Data Engineer Analyst will manage and validate large datasets for risk modeling and financial analysis, ensuring data integrity and collaboration with teams.

The Data Engineer will design and maintain ETL pipelines, optimize data models, develop exporters, and ensure reliable data services for the Lambda app in a fast-paced startup environment.

As a Data Engineer, you will build and maintain data pipelines, ensure data quality, and collaborate with teams to design data models.

Design and architect scalable backend services, develop robust governance systems, build SaaS solutions, and mentor junior engineers to enhance IoT platform capabilities.

The Data Engineer will build a creative intelligence pipeline, connecting ad data to ETL processes, enabling time-series queries, and documenting the setup for handoff.

As a Data Developer, you will create data systems, manage data pipelines, and ensure reliable data for analysis while collaborating with various teams for insights and optimization.

As a Data Engineer, you'll maintain and configure the Snowflake data platform, design integration pipelines, and innovate with new tools. Your role supports data security and performance while collaborating across teams.

The Junior Data Engineer will analyze data, design experiments, build a marketing data stack, and collaborate with teams to improve data access and insights.

Lead the design, development, and deployment of AI-driven products, manage a team of developers, and collaborate with cross-functional teams.

As a Data Engineer, you will build and maintain scalable data pipelines, ensure data quality, and collaborate on data architecture to support business intelligence.

The Senior Data Engineer designs and builds large-scale data pipelines, ensuring performance and integration. Responsibilities include data architecture, real-time analytics, and mentoring.

Lead the development of Machine Learning solutions for financial applications, collaborate with stakeholders, and conduct applied research.

As a Senior Research Engineer, you will manage data lifecycle for researchers, build a human-centric data lake, and enhance model performance through high-quality datasets in an AI-driven environment.