Senior DataOps Engineer (CloudOps, Azure/AWS)

About the role

EY is looking for a seasoned and proactive Senior DataOps Engineer to lead and manage data operations across modern Azure/AWS-based data platforms. The role takes ownership of mission-critical data pipelines, contributes to reliability engineering initiatives and guides the continuous improvement of data ingestion, transformation and monitoring. It sits within EY's Global Delivery Services, which supports consulting, assurance, tax, strategy and transactions clients.

Key responsibilities

• Ensure reliability and operational stability of enterprise data pipelines through proactive monitoring, incident management, troubleshooting and continuous improvement across cloud data platforms.
• Lead root cause analysis of recurring incidents, implement preventive actions and establish best practices to improve system resilience.
• Mentor junior engineers and guide them in incident resolution, automation and process standardization.
• Collaborate with Data Engineering, DevOps and Analytics teams to support production deployments and performance optimization.
• Identify gaps in data and operational workflows and implement scalable solutions using scripting, automation, AI-assisted capabilities, Agentic AI patterns and CI/CD practices.
• Improve runbooks, SOPs and knowledge bases, and ensure compliance with data governance, logging, observability and security best practices.
• Participate in rotational on-call duties and act as escalation point for complex operational issues.
• Oversee team management, allocate personnel, assign tasks and evaluate performance.

Required profile

• Bachelor's degree in computer science and engineering, Data Analytics or a related field, or equivalent work experience.
• 5-8 years of experience in data operations, data engineering support or application management roles.
• Experience building or supporting Agentic AI solutions integrated with enterprise data platforms, APIs, databases or operational tools, and architecting AI agents for use cases such as incident triage, root cause analysis and data quality investigation.
• Ability to apply AI-assisted automation within DataOps to improve monitoring, incident resolution and engineering productivity.
• Excellent communication and stakeholder collaboration across distributed teams, leadership mindset and attention to detail.

Required skills

• Deep understanding of cloud data services (Databricks, Snowflake, data lakes, Informatica) and data engineering operational models.
• Solid understanding of ETL/ELT design and implementation.
• Strong SQL and PySpark skills for data transformation and validation.