Top Data Engineer Jobs in Turkey

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Design, build, and optimize Snowflake-based data platforms and ELT/ETL pipelines for financial-services clients. The role involves data modeling, governance, performance and cost optimization, cloud engineering, stakeholder collaboration, solution design, technical leadership, mentoring, and client engagement. Candidates need strong Snowflake expertise, modern data engineering skills, consulting ability, and experience leading technical workstreams in Agile environments.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

Build and operate batch and streaming data pipelines, optimize SQL and data models, develop Airflow orchestration, and implement Kafka-based CDC flows. The role owns data quality, governance, observability, troubleshooting, and platform administration. It also contributes to governed LLM capabilities including retrieval-augmented pipelines, natural-language-to-SQL, and analytics automation.

Design, build, and optimize scalable GCP data pipelines, ingestion, transformation, storage solutions, and data models for analytics, machine learning, AI, and reporting. Collaborate with data scientists, ML engineers, analysts, software engineers, and business teams to deliver reliable data assets. Apply testing, CI/CD, monitoring, observability, and performance optimization practices while contributing to cloud-native platform evolution, streaming architectures, MLOps, and engineering excellence.

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 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 and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Design and build large-scale distributed data systems for blockchain analytics. Translate ambiguous product requirements into technical designs, sequence work, make architectural decisions, and own components from design through production. Work across Go, Kotlin, Rust, and Python while optimizing data ingestion, distributed processing, and data lake storage. Mentor engineers, improve design reviews and testing practices, and collaborate effectively in a fully distributed team.

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.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

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, 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.

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

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