Nissan is a pioneer in Innovation and Technology. With a focus on Mobility, Operational Excellence, Value to our Customers and Electrification of vehicles, you can expect to be part of a very exciting journey here at Nissan.
Nissan is going after a massive Digital Transformation backed by leading technologies across the organization globally. We are committed to building a diverse, entrepreneurial organization, and our current team is a strong evidence of that. Our people are what drive the business forward. At Nissan Digital, you will be part of a dynamic team with ample opportunities to grow and make a difference.
Role Overview
The role is responsible for leading the analysis of business data needs and shaping data-driven solutions that deliver measurable value aligned with the organization’s medium-term plan (MTP). This position oversees the enterprise data visualization landscape, manages operational data support activities, and drives strategic planning across the data portfolio. Working closely with senior leadership, data architecture teams, and global counterparts, the role ensures effective governance, optimal resource utilization, and alignment with global data capabilities. The individual will play a key role in enabling seamless data sharing, improving operational efficiency, and ensuring that the data function effectively supports business objectives across AMIEO regions.
Key Responsibilities
Lead the analysis of business data demand and plan data solutions that support business outcomes and deliver benefits in line with the MTP.
Oversee the data visualization ecosystem, including tools such as Tableau and Power BI, managing licensing, budgeting, and strategic direction.
Own Data Support Operations, with the vendor support lead reporting directly into this function.
Collaborate with the GM, managers, and Data Architecture teams to build the roadmap for the data domain and manage the associated project portfolio.
Act as the Data Resourcing Lead, working closely with Accenture on River governance and performance optimization initiatives.
Ensure alignment with Global IS and partner with leadership to maximize the use of NDI capabilities for AMIEO regions, including initiatives like cross-regional data sharing.
Skills and Qualifications:
Lead, mentor, and grow a team of data engineers, providing technical guidance, performance feedback, and career development support.
Oversee team workload, prioritize tasks, and ensure timely and high-quality delivery of data engineering initiatives.
Foster a collaborative, high-performance culture focused on innovation, quality, and continuous improvement.
Oversee the design, development, and maintenance of scalable, secure, and high-performing data pipelines and data platforms.
Collaborate with architects to define data architecture standards, data modelling frameworks, and best practices.
Ensure adherence to governance, metadata, and data quality standards across the platform.
Lead end-to-end delivery of data engineering projects, including planning, estimation, resource allocation, and execution.
Review and optimize ETL/ELT workflows, batch and real-time pipelines, and integrations across cloud and on-prem platforms.
Ensure robust monitoring, reliability, and performance tuning of data systems.
Partner with business stakeholders, product owners, analytics teams, and data scientists to translate requirements into scalable data solutions.
Work closely with platform engineering, DevOps, and cloud teams to drive smooth development, deployment, and operations.
Communicate progress, risks, and dependencies to leadership and cross-functional teams.
Stay current with emerging technologies, tools, and industry trends in cloud data platforms, streaming systems, and data engineering practices.
Drive innovation in pipeline design, automation, orchestration, and data lifecycle management.
Evaluate and recommend tools, frameworks, and platforms that enhance productivity and performance.
Ensure compliance with data governance, data protection, and security policies across all data engineering activities.
Oversee implementation of data quality frameworks and monitoring systems to ensure trustworthy data.
Support audit and compliance requirements, including documentation and process rigor.
Manage budgets, vendor engagements, and license usage for data engineering tools and platforms.
Drive continuous improvement in delivery processes, engineering standards, and operational efficiency.
Establish SLAs and ensure high availability and reliability of data pipelines and systems.