Cloud Data Support Engineer İş İlanı

Our client is a globally connected technology organization offering cloud-based solutions and workforce services across 170+ markets. They provide scalable platforms, compliance-ready systems, and localized support to help businesses expand internationally. ROLE Our client is seeking a skilled Cloud Data Support Engineer with strong experience in building, optimizing, and maintaining enterprise data pipelines across cloud and big data environments. The ideal candidate has hands-on expertise in ETL development, workflow orchestration, data quality management, and performance optimization using platforms such as Azure, AWS, Databricks, Snowflake, Spark, and SQL. Responsibilities Act as an escalation point for complex data pipeline, cloud workflow, and production incident issues. Troubleshoot high-priority failures across ETL pipelines, reporting workflows, and cloud-based data platforms. Work closely with product, engineering, analytics, and business teams to investigate root causes and improve system reliability. Provide technical insights to improve data architecture, pipeline performance, workflow automation, and data quality. Conduct proactive reviews of data workflows, cloud pipelines, and reporting processes to reduce operational risks. Design and support reliable data delivery processes using incremental loading, validation checks, reconciliation, and automated alerts. Create and maintain technical documentation, troubleshooting guides, and knowledge base articles for internal teams. Support structured data preparation for analytics, financial reporting, and AI/GenAI initiatives. Requirements: 3–5 years of experience in Data Engineering, Cloud Data Platforms, ETL Development, or Enterprise Analytics. Strong hands-on experience with ETL pipelines, data workflow orchestration, and production data support. Experience with cloud and big data tools such as Azure Data Factory, Databricks, Snowflake, AWS Glue, AWS MWAA, Lambda, and Spark. Strong SQL skills, including query optimization, data transformation, and performance tuning. Experience with workflow schedulers such as Autosys, Dolphin Scheduler, or similar orchestration tools. Good understanding of data validation, data quality checks, reconciliation, and incident troubleshooting. Ability to perform root cause analysis and resolve production pipeline failures under pressure. Experience supporting large-scale enterprise reporting, analytics, or financial data workloads. Familiarity with Python, Streamlit, automation scripts, or AI/GenAI data preparation is an added advantage. Strong communication skills with the ability to explain technical issues clearly to internal teams and business stakeholders. Language Requirement Complete Professional Proficiency: English and Turkish.