Manhattan WMOS Engineer
ShyftLabsSupport Manhattan WMOS implementations and operations as a WMS Engineer at ShyftLabs. ShyftLabs builds data products and digital solutions for Fortune 500 companies.
Open
Barclays is a UK-headquartered banking and financial services group serving individuals, businesses, institutions and investors. Its businesses span retail and consumer banking, corporate and investment banking, wealth and private banking, capital markets, and a specialist US consumer bank. Barclays also provides digital banking, financial research and insights, and financing programmes focused on sustainability, climate technology and green bonds. With a workforce of more than 10,000 employees, the company operates across consumer, corporate and investment finance under UK financial regulation.
HR Data Integrations Lead - Barclays, Pune Onsite
Lead governed HR data integration and distribution at Barclays in Pune. Shape data controls, architecture, quality, and downstream technology delivery.
Description
- Work with product owners and technical teams to deliver the product roadmap vision
- Serve as the subject matter expert connecting product owners with technical development teams
- Support the development and execution of product strategy and vision
- Collect and prioritise product requirements and features with internal stakeholders
- Create assessments that support continuous testing and improve product quality and performance
- Track product performance and uncover opportunities for optimisation
- Lead the secure, reliable, and governed distribution and integration of HR data for internal and external consumers
- Work with HR, Technology, and downstream consumers to guide consumer engagement
- Coordinate the effects and implementation of HR-related changes
- Reinforce accountability and controls throughout the HR data ecosystem
- Help colleagues use trusted HR data and services safely within their workflows
- Advise functional leaders and senior management on functional and cross-functional impacts
- Manage and reduce risks while strengthening controls
- Develop solutions through advanced analysis and extensive research
- Cultivate and maintain stakeholder relationships and partnerships
- Lead cross-functional teams and complex technology transformation programmes
Requirements
- Establish governance for HR data distribution, consumption, quality, metadata, lineage, and controls
- Create and embed structured engagement processes for downstream HR data consumers
- Influence the roadmaps and priorities of downstream consumers
- Implement controls that prevent and detect unauthorised HR data redistribution
- Design secure access controls for sensitive and personally identifiable HR data
- Lead multi-source data integration, transformation, mapping, and migration across complex, high-volume datasets
- Automate source-to-target validation and reconciliation processes
- Partner with Technology teams across solution design, testing, go-live, operational readiness, post-implementation support, and benefits realisation
- Manage architecture standards, programme dependencies, and control gaps
- Demonstrate excellent stakeholder management, communication, and leadership abilities
- Apply strong problem-solving skills with careful attention to detail
- Review data models and ensure alignment with enterprise architecture, privacy, and data-usage standards
- Work effectively with product managers, data architects, and engineering teams
- Bring comprehensive knowledge of data governance, data quality, metadata management, and master data management principles
- Have strong data architecture or enterprise data modelling experience, ideally in a large bank or investment institution
- Demonstrate the ability to lead cross-functional teams and complex technology transformation programmes
- Integrate governed HR data and services with Microsoft 365 tools, including Teams, Copilot, and agents
- Know Amazon Web Services, Microsoft Fabric, and Power Platform
- Use Python or comparable scripting languages
- Understand artificial intelligence, machine learning platforms, advanced analytics, and cloud-native data capabilities
- Harmonise colleague, collaboration, survey, and other datasets within a unified enterprise data landscape
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