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Job Title: Manager – Data Operations

Summary:

We are looking for a skilled and experienced Manager – Data Operations to manage the day-to-day operation of enterprise data pipelines and platforms supporting corporate finance transformation.

This role combines hands-on operational oversight, service management, and team leadership to ensure timely, accurate, and reliable data delivery. The ideal candidate will work closely with data engineering, platform, governance, and business teams to support finance processes across P2P, O2C, R2R, and FP&A.

You will be accountable for operational performance, incident coordination, data quality monitoring, and continuous improvement within the assigned service scope, helping business users and clients access trusted data within agreed service levels.

Responsibilities:

Data Operations & Technical Oversight
- Manage daily operation of batch and streaming data pipelines, including ingestion, transformation, scheduling, and delivery to downstream systems and reporting platforms.
- Monitor job execution, data freshness, processing volumes, and dependencies; coordinate reruns, backfills, and recovery using approved procedures and reconciliation checks.
- Maintain operational runbooks, standard operating procedures, job inventories, dependency maps, and escalation paths for supported data services.
- Investigate pipeline failures, data discrepancies, and performance issues using logs, monitoring tools, and SQL; engage engineering teams where code or architecture changes are required.
- Coordinate production changes and releases with engineering and DevOps teams, validating operational readiness, support handover, rollback procedures, and post-release checks.
- Lead incident triage, business-impact assessment, escalation, and service restoration; coordinate root-cause analysis and track corrective actions to closure.

Service Delivery & Operational Excellence
- Plan and prioritize operational workloads, support requests, and improvement activities to meet agreed SLAs and critical business processing deadlines.
- Implement incident, problem, change, and service request management practices; maintain effective shift handovers and on-call coverage where required by the service model.
- Track and report pipeline success rates, data freshness, SLA adherence, incident volumes, recovery times, and recurring failures; use trends to improve service performance.
- Automate repetitive checks, recovery steps, and reporting where appropriate; work with engineering teams to reduce manual effort, recurring incidents, and inefficient resource usage.
- Operate data quality checks and source-to-target reconciliations; route exceptions to accountable data owners and maintain evidence of resolution, access reviews, and approved controls.

Team Leadership & Talent Management
- Manage a team of data operations analysts and support engineers, assigning responsibilities and ensuring appropriate coverage for supported services.
- Build a culture of accountability, collaboration, service ownership, and disciplined execution, with clear expectations for operational quality.
- Manage capacity planning, onboarding, performance feedback, and training; develop cross-skilling and backup coverage for critical processes.
- Coach team members on troubleshooting, data validation, incident handling, documentation, and stakeholder communication.

Business & Stakeholder Management
- Partner with finance stakeholders, data owners, and reporting teams to understand data dependencies, business priorities, and critical reporting or period-close schedules.
- Coordinate with data engineering, platform, security, and governance teams to resolve operational issues and maintain clear ownership across service boundaries.
- Support onboarding of new data services by validating support requirements, monitoring coverage, runbooks, knowledge transfer, and acceptance criteria.
- Communicate service status, incidents, business impact, recovery estimates, and improvement progress clearly to stakeholders and management.

Data Quality & Continuous Improvement
- Work with data owners to define and maintain operational checks for completeness, accuracy, validity, consistency, and timeliness of critical datasets.
- Identify recurring data and process issues; maintain a prioritized improvement backlog and standardize reusable monitoring, reconciliation, and support practices.
- Support backup and recovery validation, continuity exercises, and retention procedures with platform and governance teams; verify data integrity following restoration.

Qualifications

Education & Experience

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
7+ years of experience in data operations, data engineering support, production support, or analytics operations, including responsibility for enterprise data services.
2+ years of team leadership or people-management experience, with a track record of coordinating operational delivery and developing support teams.

Technical Expertise

Hands-on experience supporting data workloads on at least one major cloud platform (Azure, AWS, or GCP), including monitoring, troubleshooting, and access controls.
Strong SQL skills and understanding of ETL/ELT processes, relational databases, data warehouses, and data lake or lakehouse environments.
Experience with data orchestration and processing tools relevant to the environment (e.g., Airflow, Azure Data Factory, Spark, dbt, or Kafka), including failure diagnosis and recovery.
Working knowledge of Python or shell scripting, APIs, file-based integrations, logging, and monitoring tools to automate checks and investigate data flow issues.
Understanding of data quality, reconciliation, lineage, role-based access, sensitive-data handling, and enterprise governance requirements.

Operational & Delivery Skills

Proven ability to manage production data services against SLAs, prioritize incidents by business impact, and coordinate effective service restoration.
Experience with incident, problem, and change management, release support, runbooks, and root-cause analysis; familiarity with ITIL, DevOps, or DataOps practices.
Ability to interpret operational metrics, identify performance and capacity issues, and coordinate improvements with engineering and platform teams.

Domain & Functional Knowledge

Exposure to corporate finance processes (P2P, O2C, R2R, and FP&A), finance data flows, and the importance of timely and accurate reporting is preferred.
Experience supporting finance transformation, ERP-connected data pipelines, analytics platforms, or period-end reporting operations is highly desirable.

Leadership & Behavioral Skills

Strong team supervision, workload planning, coaching, and collaboration skills.
Clear stakeholder communication, including the ability to explain incidents, business impact, and resolution progress to non-technical audiences.
Ability to remain organized under pressure, apply structured problem-solving, and balance hands-on troubleshooting with effective delegation.
High ownership mindset focused on data reliability, service quality, timely escalation, and continuous improvement.

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MSFDL-Manager - Data Audit Framework

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