Cloud Data Support Engineer

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.