Hire the Best Apache Hive Developers
in Turkey

I'm a tech & growth-oriented data professional with over 7 years of experience across the entire data lifecycle (ETL, ELT, LTL). I specialize in developing efficient data flows and effective data delivery systems to drive business value. Services Offered: ✅ Database Migration & Metadata Upgrading – Expert migration services for PostgreSQL, MySQL, NoSQL databases and more. ✅ Data Warehouse Architecture – Custom-designed, scalable data warehouse solutions tailored to your specific business requirements. ✅ ETL/ELT Development – Robust data pipeline implementation using Python, DBT, and Apache Spark. ✅ Experienced Databricks and MotherDuck solutions. ✅ Data Pipeline Architecture & Deployment – Streamlined or Batch workflow automation with Apache Airflow and Kubernetes. ✅ Application/Service Containerization – Seamless deployment and management using Docker and Kubernetes. ✅ Open Source System Management – Enterprise-level implementation of Apache Airflow, Spark, Trino, Flink, Kafka, Superset, Druid,ELK(Elasticsearch, Kibana, Logstash), Hadoop, Hdfs and Cassandra. ✅ Streaming Pipelines – High-performance data streaming solutions utilizing Kafka, Redis, Cassandra, MongoDB, and Flink with Java/Scala development. ✅ API Development – Production-ready Python APIs using FastAPI, Django, Flask, and distributed asynchronous services. ✅ Working capability any where on Cloud(AWS,GCP,Azure) or Special on-premises environment. ✅ Designing CI/CD solution's development environments. Terraform, Helm, Github Workflow, Gitlab-CI's. ✅ Implemented logging monitoring environments Grafana, Prometehus, Elasticsearch, Kibana, Logstash. Let's discuss how I can optimize your data infrastructure and unlock valuable insights for your business. Contact me today to start the conversation!

Turn messy, complex data into reliable, scalable, and production-ready systems. I am a Data Engineer and Architect with over 5 years of experience specializing in the entire data lifecycle. From building real-time streaming architectures to deploying cost-efficient cloud analytics platforms, I bridge the gap between raw data and actionable business value. With a background in both Data Engineering and Data Science, I don't just move data; I ensure it is modeled for high-performance analytics and predictive insights. Core Expertise & Services: • Data Warehouse & Lakehouse Architecture: Custom-designed, scalable solutions using Databricks (Delta Lake), and BigQuery. • ETL/ELT Pipeline Development: Robust data workflows using Python, SQL, dbt, and Apache Spark. Real-Time Streaming: High-performance systems utilizing Kafka, Flink, and Spark Streaming (Scala/Python). • Orchestration & MLOps: End-to-end workflow automation with Apache Airflow; production-grade MLOps pipelines using MLflow for experiment tracking and model lifecycle management; CI/CD pipelines with Jenkins, GitHub Actions, Terraform, and Helm. Real-time change data capture (CDC) architectures with Debezium integrated into Kafka-based systems. • Infrastructure & DevOps: Production-ready containerization via Docker & Kubernetes across AWS, GCP, and Azure, or specialized on-premises environments. • Database Migrations: Seamless metadata upgrading and migration for PostgreSQL, MySQL, and NoSQL systems. • AI Engineering & LLMOps: Building production-ready LLM-based systems, RAG architectures, and vector data modeling integrated with MLflow for scalable AI deployment. • AI-Driven Automation & Orchestration: Designing and deploying autonomous AI agents and advanced workflow automation using n8n. Expert in bridging LLMs, vector databases, and enterprise data pipelines into production-ready, self-hosted or cloud-based autonomous systems that orchestrate complex business logic. Technical Stack: Languages: Python (FastAPI), Scala, SQL. Big Data Tools: Spark, Trino, Flink, Hadoop, Hive. Data Ops, MLOps & Automation: Airflow, dbt, n8n (AI Agent Workflows), Kafka, Debezium (CDC), MLflow, Jenkins, CI/CD pipelines. Cloud: AWS, GCP, Azure, MinIO. Monitoring & Logging: Elasticsearch, Kibana Visualization: PowerBI, Tableau, Looker Studio Proven Impact: Award-Winning Solutions: Led a freight price estimation project that achieved 78% accuracy, winning the Ministry of Trade Digital Transformation Award. Cost Efficiency: Reduced annual operational costs by $150K through optimized data architecture and predictive modeling. Global Collaboration: Extensive experience working with international teams across Europe and Turkiye, combining technical leadership with business strategy. Whether you need a complete migration to the cloud or a real-time analytics dashboard, I am here to build the engine that powers your growth. Let's discuss how I can optimize your data infrastructure and unlock valuable insights for your business. Click "Invite to Job" or "Message" to start the conversation!

Software Engineer with 9 years of complex development on all operating systems. Expertise in the design and development of systems and software for high-speed data acquisition, distributed systems, DevOps and cloud computing. Solid background in the technology of containers, big data, microservices, testing and analysis.

As a Data Engineer, I build and manage robust, end-to-end data systems designed for scalability and real-time performance. My experience covers the full data lifecycle: from automated data scraping and ingestion, through complex batch and stream processing, to the development of custom APIs and customer-facing analytical applications. I am proficient in cloud-native environments, leveraging containerization and orchestration to create resilient, microservice-based architectures. I focus on writing clean, efficient code and selecting the best technologies to solve complex data challenges in a practical and maintainable way. Technical Expertise Data Streaming & Real-Time Processing: Apache Kafka: Fluent in designing and operating high-throughput streaming platforms, including Confluent Kafka and Kafka Connect (Source/Sink connectors) deployed on Kubernetes. Apache Flink: Development of stateful, real-time stream processing applications. Apache Spark: Building and optimizing Spark Structured Streaming pipelines. Batch Processing & Big Data: Apache Spark: Extensive experience in large-scale batch ETL processing using PySpark across diverse data sources. Big Data Ecosystem: Proficient with Databricks, Hadoop, and Apache Hive. Data Orchestration & Workflow Automation: Apache Airflow: Designing, scheduling, and monitoring complex DAGs. Specialized experience using the KubernetesPodOperator for scalable, containerized task execution within Rancher-managed clusters. Apache NiFi: Building and managing automated data flows. Cloud, Containerization & DevOps: Cloud Platforms: AWS (S3, Redshift, Glue, Athena), GCP. Containerization: Docker, containerd. Orchestration: Kubernetes (K8s), including hands-on experience with minikube and k3s, managed via Rancher. Architecture: Designing and implementing Microservice Architectures. CI/CD: Jenkins, GitHub Actions, Git (GitHub, Bitbucket), version control, and branching strategies. Databases & Data Warehousing: NoSQL: Cassandra (daily operational use in production clusters), MongoDB, CouchDB. Analytical (OLAP): ClickHouse, Trino (PrestoSQL), Amazon Redshift. Relational (SQL): PostgreSQL, MySQL. Storage & Caching: Redis for application caching layers, MinIO for S3-compatible object storage. API Development & Embedded Analytics: API Development: Building performant web services and APIs using FastAPI and Python. Proficient with API testing tools like Postman. Embedded Analytics: Advanced development with Apache Superset, utilizing its REST API and Security API to build secure, custom shell applications for customer-facing analytics. Monitoring, Logging & Visualization: Log Management: Full implementation of the ELK Stack (Elasticsearch, Logstash, Kibana) for log aggregation, analytics, and building monitoring dashboards. Metrics & Visualization: Grafana, Tableau, Power BI, Apache Superset. Programming, MLOps & Project Management: Languages: Python (incl. Pandas, NumPy), SQL, Bash. MLOps: MLflow for managing the machine learning lifecycle. Project Management: Atlassian Jira for Agile/Scrum sprint planning and task management. Data Scraping: Designing and operating automated data scraping pipelines using Puppeteer, Selenium, Playwright, and FlareSolverr. Notable Projects - Reddit Data Pipeline Developed an automated pipeline for extracting, transforming, and analyzing Reddit data. Leveraged NiFi, MinIO, Trino, and Tableau/Superset for a complete ETL process, enabling dynamic querying and visualization of high-velocity data streams. - OTP Data Pipeline Engineered a real-time pipeline for secure OTP generation and notifications, utilizing Kafka, Airflow, Cassandra, and MongoDB. Integrated email and Slack/Discord notifications for streamlined monitoring. - Yelp Sentiment Analysis Created a real-time streaming pipeline for Yelp reviews using Spark, Kafka, and Hugging Face’s DistilBERT. Visualized sentiment analysis insights in Kibana, showcasing the integration of machine learning with data engineering. - ELK Stack Log Management Set up a secure and scalable multi-node ELK Stack for log analytics, employing index lifecycle policies and creating drill-down dashboards in Kibana. Certifications and Education - IBM Data Engineering Specialization (Coursera) - Apache Kafka, ELK Stack, and NoSQL Databases (BTK Academy) - Machine Learning Specialization (Stanford, Coursera) - Data Engineering Nanodegree (Udacity)

After 15 years of experience in Presales, I decided to change my field. Therefore, I turned to the software field. I work together with the solution teams. I support presales Cloud, Devops, Big data and Data science projects to create proposals. I completed the course Certified Kubernetes Administrator (CKA) with Practice Tests 22.5 hours. I have knowledge container technology, kubernates, docker, Continues Integration Gitlab, Linux. Creating pod, managing worker node, master node, micro service. I operate Git repository at Gitlab , Github to control version for dockerized Iot project. Gitlab(CI/CD) projects: gitlab . Also, I made a Big Data Analysis of Attack Detection project using UNSW-NB15 dataset at UEL Master. The UNSW-NB15 dataset is a widely used network intrusion detection dataset that contains various types of network attacks. It covers a wide range of attack types. it has been worked at Hive with Hadoop User Experience (HUE) using default database at Cloudera Hive. It was used Apachi Spark to analyze the dataset at Jupyter Notebook. Now I have took traning at Miuul Data Science Bootcamp for 4 month. I am very eager to work in the data field.I can start working as a junior.Numerical and categorical variable analysis was performed by extracting “Titanic” dataframe from Seaborn in Pandas and Numpy on Jupyter Notebook.