EY is seeking a skilled Manager – Data Operations to lead the daily operations of enterprise data pipelines and platforms vital for corporate finance transformation. This role blends hands-on oversight, service management, and team leadership to ensure accurate and timely data delivery. You will collaborate with data engineering, platform, governance, and business teams to support crucial finance processes across P2P, O2C, R2R, and FP&A.

Accountable for operational performance, incident coordination, data quality, and continuous improvement, you will empower business users and clients with trusted data within agreed service levels, contributing to EY's mission of building a better working world.

Oversee daily batch and streaming data pipelines, including ingestion, transformation, and delivery. Monitor job execution, data freshness, and processing volumes, coordinating reruns and backfills as needed. Maintain operational documentation like runbooks and standard operating procedures.

Investigate pipeline failures and data discrepancies, engaging engineering teams for complex issues. Coordinate production changes and releases, ensuring operational readiness and performing post-release checks. Lead incident triage, business impact assessment, and service restoration efforts, driving root-cause analysis.

Plan and prioritize operational workloads to meet SLAs and critical business deadlines. Implement incident, problem, change, and service request management practices. Track and report on key metrics like pipeline success rates and SLA adherence to drive service performance improvements. Automate repetitive tasks and work with engineering teams to reduce manual effort and recurring incidents.

Operate data quality checks and source-to-target reconciliations, routing exceptions to data owners. Manage a team of data operations analysts, fostering accountability and collaboration. Develop capacity planning, onboarding, performance feedback, and training plans, ensuring cross-skilling and backup coverage.

Partner with finance stakeholders and data owners to understand data dependencies and business priorities. Coordinate with cross-functional teams to resolve operational issues. Support the onboarding of new data services by validating requirements and knowledge transfer. Communicate service status, incidents, and improvement progress clearly to stakeholders.

A Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, coupled with at least 7 years of experience in data operations, production support, or analytics operations, including managing enterprise data services. Proven experience of over 2 years in team leadership or people management is essential.

Demonstrated hands-on experience supporting data workloads on major cloud platforms (Azure, AWS, or GCP) is required. Proficiency in SQL, understanding of ETL/ELT processes, relational databases, data warehouses, and data lake/lakehouse environments is crucial. Experience with data orchestration tools like Airflow, Azure Data Factory, Spark, dbt, or Kafka is necessary.

Working knowledge of Python or shell scripting, APIs, file-based integrations, and logging/monitoring tools is expected. A solid understanding of data quality, reconciliation, lineage, access controls, and enterprise governance requirements is beneficial. The ability to manage production data services against SLAs, prioritize incidents by business impact, and coordinate service restoration is key.

Familiarity with incident, problem, and change management, release support, runbooks, and root-cause analysis, along with ITIL, DevOps, or DataOps practices, is advantageous. Exposure to corporate finance processes such as P2P, O2C, R2R, and FP&A is preferred, as is experience supporting finance transformation or ERP-connected data pipelines.

Strong team supervision, workload planning, coaching, and collaboration skills are required. Excellent stakeholder communication, the ability to explain technical concepts to non-technical audiences, and structured problem-solving skills are essential. A high ownership mindset focused on data reliability and service quality is paramount.