Top Data Engineer Jobs in Turkey

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.

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.

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.

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 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.

Design, implement, document, and validate the database and data management layer for a cloud-based geospatial immunization planning solution. Responsibilities include data modeling, PostgreSQL/PostGIS implementation, ETL/ELT pipelines, geospatial data harmonization, quality assurance, lineage tracking, access controls, performance optimization, and cloud deployment coordination. The consultant will integrate heterogeneous public health and geographic datasets to support measles vaccination gap analysis, campaign microplanning, indicators, and monitoring outputs.

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.

Build, automate, and operate enterprise-scale data platforms using Databricks, Snowflake, Microsoft Fabric, Kubernetes, and cloud technologies. Responsibilities include Infrastructure as Code, CI/CD, GitOps, platform provisioning, reliability engineering, observability, security, governance, incident remediation, performance optimization, and FinOps. The role supports multi-environment deployments and self-service capabilities for data engineering and analytics teams.

Lead Customer 360 data engineering initiatives across Customer and Sales domains. Design data integration, ingestion, curation, governance, quality, harmonization, and common data models. Build scalable cloud-based data capabilities and integration pipelines while supporting data products, use cases, and analytics. Collaborate with business owners, regional partners, and technology teams to define strategy, roadmaps, prototypes, and MVPs, serving as a subject matter expert in Customer 360 data.

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.

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.