Top Data Engineer Jobs in Turkey

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.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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

Design, build, and maintain scalable cloud-native data pipelines and storage on GCP. Optimize ingestion, transformation, and data models for analytics and AI, implement CI/CD and testing, monitor platform reliability, and support MLOps and ML workflows.

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.

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.

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.

Design, build, and maintain Power BI dashboards, semantic models, datasets, and automated reporting. Develop scalable data pipelines and automation using SQL, Alteryx, Python, and Microsoft Power Platform. Ensure data quality, validation, reconciliation, governance, and partner with stakeholders to enable self-service analytics and decision-making across commercial, finance, and supply chain functions.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Lead migration from Snowflake to the client platform, build AI-native data migration capabilities, design and maintain scalable data pipelines and platforms, propose improvements, and collaborate with senior engineers, product/project managers, and data analysts.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

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.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Maintain and modernize legacy ETL and data warehouse processes; develop with Oracle, Teradata, SQL/PLSQL and DataStage/Informatica/Ab Initio; build Apache Spark data pipelines; read/write AWS S3; lead legacy-to-modern migration, PoCs, architecture, and technical standards for data teams.

The Software Engineer will manage data collection for model training, optimize cloud infrastructure, and collaborate on the dataset roadmap for AI products.

The Software & Big Data Engineer will design, develop, and validate automation solutions for mobile networks, interfacing with external systems and supporting product customization and deployment.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

The Principal Engineer in Data Infrastructure will oversee database and data warehousing infrastructure, optimize performance, and integrate new technologies while supporting data analysis and engineering efforts.