Director, Data Delivery in London

The Director, Data Delivery is a strategic and execution-focused leader responsible for enabling Wella’s data-driven transformation through scalable, high-quality, and AI-ready data platforms. This role leads the delivery and operationalization of modern data solutions, ensuring that enterprise data is accessible, governed, and optimized for advanced analytics and AI use cases.

Working in close partnership with the Data architecture and Enterprise Architecture teams, this position translates architectural designs into high-quality, production-ready data products. The role is accountable for executing against defined architectures, ensuring adherence to standards while delivering reliable and scalable solutions.

As a key partner to business and technology stakeholders, this role is responsible for delivering robust data pipelines, platforms, and products that power analytics, reporting, and machine learning initiatives. It has deep expertise in Databricks and modern data engineering practices, including environment setup, ingestion frameworks, ML lifecycle management (MLflow), and scalable coding practices.

This role leads delivery through internal and external vendor squads and is accountable for ensuring high performance, quality, and timeliness of delivery. It plays a critical role in driving vendor accountability, enforcing engineering standards, and ensuring outcomes aligned with business and technical expectations.

• Data Platform Architecture, Delivery & Engineering Execution & Operations
• Own the end-to-end delivery of data solutions on platforms (Databricks & SAP), ensuring alignment with enterprise architecture principles.
• Oversee setup, configuration, and optimization of data environments to ensure performance, scalability, and reliability.
• Drive implementation of scalable ingestion processes (batch and real-time), transformation pipelines, and curated data layers.
• Establish and enforce standards for coding, CI/CD, testing, and deployment across all data delivery teams.
• Ensure consistent implementation of MLOps & MLflow for model lifecycle management and reproducibility.
• Establish & Lead Data Solution Architecture practice to ensure design integrity and proper execution of data architecture patterns.
• Vendor Delivery Management & Governance
• Lead delivery through third-party vendor squads, ensuring high-quality, timely, and cost-effective execution.
• Define delivery expectations, SLAs, KPIs, and acceptance criteria for all vendor teams.
• Hold vendors accountable for performance, quality, and adherence to engineering and governance standards.
• Establish robust governance frameworks, including sprint reviews, delivery checkpoints, and performance tracking.
• Proactively manage delivery risks, issues, and dependencies, ensuring rapid resolution and minimal business impact.
• Drive continuous improvement in vendor performance, productivity, and delivery outcomes.
• Solution Delivery, Operations & Lifecycle Management
• Ensure end-to-end accountability for the lifecycle of data products—from ingestion to consumption.
• Establish & manage disciplined release management, testing, and deployment practices.
• Ensure platform reliability, stability, and scalability across all data workloads.
• Optimize data processing performance and cost efficiency.
• Act as the escalation point for delivery and operational issues related to data pipelines and platforms.
• Data Governance, Quality & AI Readiness
• Establish and implement enterprise data governance standards and policies.
• Implement and enforce data classification standards aligned with enterprise data governance policies.
• Drive data quality frameworks, including monitoring, validation, and issue remediation.
• Ensure data is structured, cataloged, and accessible for analytics and AI use cases.
• Champion the creation of AI-ready datasets through standardization, cleansing, and enrichment.
• Collaborate with governance, security, and compliance teams to meet regulatory and privacy requirements.
• Business Engagement & Value Delivery
• Partner with business stakeholders and analytics teams to translate requirements into scalable data solutions.
• Enable advanced analytics, reporting, and AI initiatives through high-quality, reliable data delivery.
• Drive prioritization of data initiatives aligned with business value and outcomes.
• Define and track delivery metrics, ensuring transparency into progress and value realization.
• Vendor & Partner Ecosystem Management
• Manage relationships with external delivery partners and data platform vendors (e.g., Databricks).
• Ensure alignment between vendor capabilities and Wella’s delivery and quality expectations.
• Collaborate with Procurement and IT leadership on contract management, performance reviews, and cost optimization.
• Ensure compliance with InfoSec, data privacy, and enterprise technology standards across all vendor-delivered solutions.
• Leadership & Organizational Development
• Provide leadership across multiple vendor squads, ensuring alignment, coordination, and delivery excellence.
• Establish clear delivery governance, operating models, and ways of working for external teams.
• Act as the internal point of accountability for all data delivery outcomes.
• Foster a culture of accountability, quality, and continuous improvement across internal and external teams.
• Develop internal capabilities to effectively manage and govern vendor-led delivery models.

• Very strong technical & management expertise in modern data architecture, engineering and operational efficiencies.
• Proven experience architecting and developing AI enabled accelerators for various data management use cases (ingestion, DQ, data mapping, ETL/ELTs etc.).
• Databricks Platform Expertise – deep experience with Databricks, including environment setup, architecture, tooling, engineering, optimization, and operational delivery.
• Data Engineering Delivery – strong expertise in implementing ingestion pipelines, transformations, and scalable data products.
• ML Lifecycle Enablement – hands-on experience with MLflow and operationalizing machine learning workflows.
• Vendor & Delivery Management – proven ability to manage third-party vendors, enforce accountability, and deliver through external squads.
• Governance & Accountability – strong experience establishing delivery governance, KPIs, and performance management frameworks.
• Data Governance & Quality – experience implementing data classification, quality controls, and governance processes.
• AI Data Enablement – proven ability to prepare and deliver AI-ready datasets for advanced analytics.
• Technical Depth & Coding – strong programming skills (Python, SQL, Spark) with focus on quality and maintainability.
• Stakeholder Management – ability to translate business needs into actionable data delivery outcomes.

• Education: Bachelor's or master's degree in computer science, data engineering, information systems, or a related field. Advanced certifications in data platforms, cloud technologies, or AI/ML are a plus.
• Experience: 10-15 years of experience in data engineering, data platforms, or analytics delivery roles. Proven hands‑on experience with Databricks, including environment setup, ingestion frameworks, and optimization. Strong experience implementing MLflow and supporting machine learning lifecycle management. Demonstrated experience managing delivery through third‑party vendors or system integrators. Proven leadership in overseeing multiple delivery squads (vendor or hybrid models). Strong experience in data governance, data classification, and data quality implementation. Proven ability to deliver AI‑ready data solutions at scale. Strong coding experience in Python, SQL, and Spark‑based processing. Experience with SAP data ecosystems, including SAP BDC, Datasphere, and SAP data integration. Familiarity with enterprise ERP data models and business process data domains.