Top Data Engineer Jobs in Turkey

Design, build and optimise cloud-native data platforms and scalable ELT/ETL pipelines using Snowflake. Integrate enterprise data sources, ensure data quality, governance and performance, support client engagements with technical leadership, mentor junior staff, and deliver solutions using modern data engineering tools (dbt, Airflow, Kafka, Snowpark, Snowpipe).

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.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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.

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.

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

Design, build, and scale high-performance data pipelines and infrastructure (ingestion, transformation, storage, modeling, serving) using ClickHouse, Postgres, Python and dbt. Manage data quality, reliability, and observability at terabyte and 500M+ address scale, collaborate with researchers and product, mentor engineers, and leverage AI tools to accelerate work.

Own and evolve the core data platform: warehouse design, dbt transformations, and production AWS pipelines. Refactor and harden API-driven data flows, ensure observability and testing, partner with backend and product teams, and contribute to data-aware backend features to deliver reliable, product-focused data.

Design, build and maintain Zero-ETL scalable data pipelines and Redshift data models. Develop and manage Airflow workflows, integrate multiple data sources, ensure data quality and availability, optimize queries and warehouse performance, monitor and resolve platform issues, document pipelines and collaborate with analysts and stakeholders on architecture and analytics needs.

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.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

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 role involves finding audio data sources, extending cloud infrastructure on GCP, and collaborating with AI scientists to improve data for model training.

Build end-to-end features for a data orchestration platform across Java/Micronaut backend and Vue 3/TypeScript frontend. Implement data-aware orchestration (assets, lineage, freshness, quality), create integrations/plugins (dbt, Fivetran, Airbyte, warehouses), improve UI (flow editor, topology, dashboards), collaborate on 8-week releases, review PRs, and engage with the open-source community.

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Build a greenfield data platform and ingestion pipelines for payments, marketing, and operational data across AWS and GCP; implement a Lakehouse with medallion-layer transformations in dbt/SQLMesh (Redshift/BigQuery); own orchestration (Dagster/Airflow), lineage, backfills, retries, alerting; apply IaC with Terraform and cost controls; operate autonomously in a remote, async environment.

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.

Lead site-based CSA activities for data centre projects: manage contractors, ensure compliance with design, safety and quality, coordinate stakeholders, produce reports, and support project leadership.