Top Data Engineer Jobs in Turkey

As a Data Engineer at CEPRES, you will develop and maintain data pipelines, collaborate with software teams, and optimize data models, ensuring data integrity and accessibility.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

The Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

Design, model, and develop AWS-based data ecosystems and pipelines. Gather and document business and technical requirements, optimize data storage (lakes/warehouses), tune performance, troubleshoot processing and infrastructure issues, integrate diverse data sources, mentor junior engineers, and engage directly with clients.

The Cloud Data Engineer is responsible for designing and optimizing data models, developing cloud data solutions using Snowflake, and collaborating with application development teams to implement data strategies.

Build and deploy hybrid data integrations for enterprise customers: implement ClickHouse or warehouse-native pipelines (Snowpark/BigQuery), construct AI semantic layers and canonical growth models, partner with AI discovery agents to accelerate onboarding, and lead technical enterprise deployments ensuring performance and reasoning consistency.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

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

Lead and own the data science function: design and build production ML systems (recommendation, forecasting, segmentation), develop configurable multi-tenant models, run rigorous experimentation and causal analyses, connect model outputs to business impact, set scientific standards, and hire and mentor a high-velocity data science team.

Own and evolve Enode's data platform and pipelines to support the Flex product: build reliable datasets, ensure data quality and monitoring, enable self-serve analytics, and partner with data science on feature pipelines and model readiness. Drive pragmatic platform improvements, documentation, and tooling to scale data reliability and trust across delivery, billing, and reporting.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Design and maintain scalable data pipelines and platforms, lead migration from Snowflake to client service, propose improvements, drive initiatives, and collaborate with senior engineers, product/project managers, and data analysts.

Lead and develop a chapter of Data & AI engineers, define engineering standards and reusable patterns, ensure scalable and secure delivery of data pipelines, ML models and AI solutions, contribute hands-on to high-impact initiatives, align capacity with business priorities, and drive capability building and continuous improvement.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

As a Senior Software Engineer, you will design and maintain data infrastructure for protein engineering, ensuring secure data pipelines and compliance, while collaborating with multi-disciplinary teams.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Support and optimize large-scale Spark/YARN jobs and storage (S3/EMR). Build job-profiling, cost-analysis, AI-assisted automation, monitoring/alerting (Prometheus, Flink/Spark), and internal ops tooling to improve stability and reduce costs.

As a Senior Data Engineer, design and maintain scalable data pipelines, optimize infrastructure, and ensure data governance for real-time ML models.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.