Top Data Engineer Jobs in Istanbul

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.

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, 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.

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.

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 and build high-performance Java microservices and data pipelines to parse, transform, and analyze multi-chain on-chain data in real time. Integrate with Kafka, Redis, RDS, and data lake systems; optimize low-latency computation for token prices, swaps, liquidity metrics; ensure fault tolerance, observability, and alignment with long-term data platform strategy.

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.

Design, develop, and maintain high-performance, highly available backend services and APIs for Big Data products using Java/Python. Manage full service lifecycle, capacity planning, profiling/benchmarking, troubleshooting, security, and system architecture. Contribute to open-source big data projects and build scalable tools for global operations.

Design and build high-throughput reconciliation systems and data pipelines. Implement real-time (Flink) and batch (Hive) workflows, write complex SQL for validation and anomaly detection, collaborate with Finance/Risk/Product on reconciliation rules, optimize for low latency, and leverage AI/LLM tooling to automate triage and improve operations.

Lead design and implementation of scalable, high-performance backend services for traditional and AI-powered search. Own search architecture, optimize indexing/retrieval/ranking, build reusable frameworks, collaborate with data scientists on LLM-enabled search and agents, and deliver robust, low-latency solutions.

Design and develop high-performance backend services for an AI-powered trading assistant: build AI agent execution, trading order execution, AI sub-account and API key lifecycle management, integrate Binance trading APIs, implement skills/tool-calling frameworks, and ensure scalable, low-latency real-time data pipelines, observability, and security.

Design and develop cloud-native backend services in Rust to process terabytes of data daily. Build and operate data-intensive microservices on AWS and Kubernetes, integrate with distributed databases and messaging systems (Kafka/RabbitMQ), and collaborate across teams to deliver observability features.

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.

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.

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.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Design, develop, and deploy production-grade ML and AI solutions including LLM fine-tuning, RAG architectures, NLP pipelines, time-series forecasting, supplier risk and anomaly detection, scalable data pipelines, and RESTful integrations with enterprise systems.

Design and develop a big-data processing engine and heavy ETL pipelines (including geo-location analysis) across cloud and on-prem. Build distributed, partitioned pipeline architectures using Spark/PySpark, Scala, Airflow, Kubernetes, and cloud platforms; follow Agile, clean-code, and software design best practices while supporting deployments and client-facing interactions.

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.

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.