Tpm Manager

Key Responsibilities:

Delivery Management

• Own end-to-end delivery of data engineering, analytics, and      platform modernization initiatives.
• Manage project scope, timelines, budgets, risks, dependencies,      and resource allocation.
• Establish delivery governance and ensure projects meet quality,      schedule, and business objectives.
• Drive delivery planning, status reporting, issue resolution,      and executive communication.

Team Leadership & People Management

• Lead, mentor, and develop a team of Data Engineers and      Technical Leads.
• Drive workforce planning, onboarding, capability development,      and succession planning.
• Establish performance expectations and promote engineering      excellence.
• Foster a culture of accountability, collaboration, continuous      improvement, and innovation.

Data Platform & Engineering Governance

• Govern the implementation and support of enterprise data      platforms, data lakes, Lakehouse architectures, and analytics solutions.
• Ensure adherence to architecture standards, security policies,      development guidelines, and operational processes.
• Lead architecture and design review processes to ensure      solution quality and compliance.
• Drive standardization, reuse, automation, and operational      efficiency across the data ecosystem.

Stakeholder & Business Engagement

• Partner with business leaders, product owners, enterprise      architects, and technology teams to define priorities and delivery      roadmaps.
• Translate business objectives into executable delivery plans.
• Manage stakeholder expectations and ensure alignment between      business outcomes and technical solutions.
• Provide regular updates to leadership on delivery status,      risks, and strategic initiatives.

Data Quality, Compliance & Risk Management

• Ensure implementation of data governance, security, privacy,      and compliance requirements.
• Establish controls for data quality, monitoring, and      operational readiness.
• Identify delivery risks and implement mitigation plans      proactively.

Analytics & AI Enablement

• Enable enterprise reporting and analytics through optimized      data products.

• Support advanced analytics, machine learning, and AI      initiatives by providing trusted and scalable data foundations.

• Evaluate and adopt emerging technologies that improve      engineering efficiency and business value.

• Strong understanding of:

• Azure Data Platform

• Databricks

• Azure Data Factory (ADF)

• PySpark

• SQL

• Lakehouse/Data Lake architectures

• Data Governance and Data Quality frameworks