Top Data Analyst Jobs in Turkey

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

The Data Analyst will build and maintain dbt models, analyze billing and SaaS metrics, monitor data quality, maintain Metabase reporting, and support acquisition due diligence. The role involves tracing data from source systems through ingestion and transformation, resolving production issues, validating results, and communicating findings to technical and business stakeholders. The analyst will also maintain Airbyte integrations, document data definitions, and use AI tools responsibly in daily data work.

Analyze product and marketing data for mobile games, build dashboards and reports, write SQL queries, verify data integrity, evaluate initiatives, and provide actionable recommendations. The role also involves configuring analytics and visualization tools, synthesizing information from multiple sources, and promoting data-informed decision-making across the organization.

Entry-level Product Data Analyst internship supporting product analytics: build and analyze conversion funnels, evaluate A/B tests, create dashboards, respond to ad-hoc data requests, write SQL pipelines, monitor data quality, and translate findings into visual summaries to inform product decisions.

Internship data analyst supporting dashboard monitoring, user behavior and content ecosystem analysis, A/B test evaluation, campaign performance analysis, data quality assurance, and metric documentation. The role uses SQL and Excel to derive insights on engagement, retention, conversion, and creator ecosystems, while collaborating with product and operations teams in a blockchain and digital assets environment.

Collect, analyze, and interpret data to support data-driven decision-making and strategic business initiatives. Create actionable insights and compelling visualizations using SQL, Tableau, and Power BI. Collaborate directly with the CTO and organizational teams in an exclusively on-site Antalya office environment.

Analyze large-scale data to generate insights for product, marketing, and business strategy. Build and maintain dashboards and reports using analytics tools, perform ad-hoc analyses, communicate findings clearly, and collaborate cross-functionally to define data requirements and support fast decision-making.

Lead marketing analytics and M&A data integration initiatives. Harmonize KPIs, attribution, reporting, and data landscapes; build scalable marketing data models and ELT pipelines; create dashboards; analyze advertising and mobile measurement platforms; deliver executive insights; and lead cross-functional analytics projects involving multiple business units.

Conduct exploratory and business data analysis, build and maintain dashboards, translate stakeholder needs into data work, support experiments and investigations, and document queries and standards. Present actionable findings clearly to nontechnical colleagues while ensuring data accuracy and reliability. The role includes working with SQL and data visualization, with opportunities to use Metabase, dbt, Python, or R.

Analyze large-scale on-chain data to detect anomalous transactions, develop detection mechanisms and risk monitoring frameworks, apply supervised and unsupervised ML, identify malicious accounts via community-discovery algorithms, and design integrated on-chain/off-chain prevention and control systems.

Analyze large datasets to identify trends and deliver actionable insights; design and monitor KPIs; build and maintain dashboards and reporting frameworks; conduct ad hoc analyses; ensure data quality and support reporting infrastructure in partnership with product and business teams.

Early-career Data Analyst responsible for designing and owning core metrics and dashboards, analyzing transaction/customer/credit data to inform credit, fraud, AML, portfolio and growth decisions, building data pipelines and automations with SQL and Python, partnering with cross-functional teams, and owning compliance reporting. Emphasis on turning ambiguous problems into reliable analytical systems and applying GenAI/LLMs for practical automation.

Analyze and instrument offsite discovery and SEO/GEO performance for ecommerce sites. Build integrations, ETL pipelines, and models to track organic and AI-driven traffic. Create tooling and frameworks for technical site audits, landing page modelling, SEO experiments, and enablement for product and customer teams.

Support multilingual data quality by analyzing and cleaning text data, developing parsers, evaluating text classifiers, translating taxonomies (Skills, Titles, Occupations), and partnering with Linguistics and Engineering to deliver high-quality global data.

Early-career role analyzing PB-scale on-chain, transactional and customer data. Perform data engineering, ETL, querying, build visualizations and ML models, translate insights for teams, manage reporting environment, secure confidential data, and troubleshoot reports/databases.

Support development and maintenance of SQL-based data pipelines and analytics workflows. Assist data standardization, build and optimize data models, dashboards, and reports. Perform ad-hoc analyses, monitor data quality, implement validation logic, and collaborate on cross-functional data and risk projects under mentorship.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Build and maintain dashboards, analyze large datasets to surface actionable insights, ensure data accuracy and integrity, and develop scalable analytics deliverables to support product and marketing decisions.

Partner with Marketing, Sales, Finance, Product and other teams to turn business questions into actionable analysis. Own end-to-end analytics: build datasets and dashboards (dbt, LookML), maintain metric definitions and caveats, use and curate agent tooling for trustworthy answers, and contribute small data platform and pipeline improvements.

Analyze billing and support performance, build and maintain data pipelines, apply statistical forecasting and A/B testing, collaborate cross-functionally, document analyses, and provide recommendations to reduce financial risk, disputes, churn, and operational inefficiencies.

The Data & Monetization Analyst will design A/B tests, analyze KPIs, develop player segmentation tools, and support multiple teams with data insights.

Analyze large-scale user and product data, design and evaluate A/B tests, define and validate product and business metrics, build dashboards and reports, and partner with engineers, data scientists, product, and customers to translate insights into product improvements.

Lead consolidation, validation and integrated reporting of project performance data across engineering, procurement, fabrication and project controls. Develop executive dashboards and advanced analytics (including Monte Carlo simulation and contingency analysis), support governance, and partner with multidisciplinary project teams on major EPC, LNG and energy infrastructure projects.

Analyze PB-scale trading, transactional, operational and customer data; build visualizations, ML models and experiments; translate findings into actionable recommendations for trading, operations, product and executives in a fast-paced environment.

Validate and reconcile fund and financial data, investigate and resolve data issues, own daily operational workflows, partner with data and engineering to improve quality, and build/document scalable procedures to support private-markets operations.