QData - VP of Data

About the Role

To lead QData's data organization, encompassing data engineering, data product development, and data governance. The role's purpose is to establish a robust data platform and analytics capability for ADQ enabling data-driven decision making, advanced analytics, and AI/ML initiatives. This executive defines the data strategy, oversees the building of data infrastructure and products, and ensures data quality, governance, and compliance. To serve as the champion for treating data as a strategic asset and delivers platforms and services that transform raw data into actionable insights and innovative data products.

Key Responsibilities:

• Data Strategy & Architecture: Define QData's data strategy and target state architecture for data platforms (covering data lakes, warehouses, pipelines, and analytics/ML platforms). Set the vision for how data is collected, stored, managed, and used across ADQ's businesses, ensuring scalability, security, and alignment with enterprise architecture principles.
• Data Platform Development: Oversee the design and development of core data infrastructure including cloud-based data lakes/warehouses, ETL/ELT pipelines, real-time streaming systems, and analytics tools. Ensure that the data engineering team delivers a high-performance platform that serves multiple use cases (BI reporting, advanced analytics, machine learning) and is well-integrated with QData's other systems and solutions.
• Data Products & Analytics: Guide the Data Products team in building data-driven products and solutions (e.g., dashboards, predictive models, data APIs) that address key business questions. Prioritize development of analytics use cases and ensure the team is effectively turning data into insights and value for end-users. Champion the adoption of self-service analytics tools and data democratization across the organization.
• Data Governance & Quality: Implement a robust data governance framework in conjunction with the Data Governance team. Establish policies and standards for data quality, metadata management, master data management, and access controls. Ensure compliance with relevant data protection regulations (e.g., GDPR) and internal policies, minimizing risk related to data security and privacy.
• Cross-Functional Leadership: Collaborate closely with the Technology and Solutions Delivery VPs to ensure data considerations are embedded in all projects. Advise on how data can support or enhance solution deliveries in various domains (for example, providing analytics for a healthcare project or integrating data science into a logistics solution). Act as the key evangelist for data-driven culture, working with business leaders to identify new opportunities where data analytics can drive performance improvements.
• Research & Innovation: Stay abreast of latest trends in data, AI, and analytics (e.g., big data technologies, AI/ML advancements, data mesh architectures). Evaluate new tools, platforms, or approaches (such as AI services, data virtualization, automation in data engineering) and incorporate relevant innovations into QData's roadmap. Foster experimentation through pilot projects or proofs-of-concept to continuously improve data capabilities.
• Budget & Talent Management: Manage the budget for the data division, including investments in technology and personnel. Build and grow a high-caliber data team by attracting top talent in data engineering, data science, and governance. Mentor department heads (Data Engineering, Data Products, Data Governance) and ensure the team structure and skills remain aligned with evolving business needs.

Required Technical Competencies:

• Data Management (DATM) Level 6: Mastery in governing and controlling organizational data assets ensuring integrity, availability, and security of data across systems. Ability to set data standards and lifecycle policies at an enterprise level.
• Data Engineering (DENG) Level 6: Expertise in designing and implementing complex data pipelines, integration architectures, and storage solutions (relational and NoSQL databases, big data platforms) Capable of leading teams in modern data engineering practices (cloud-based, distributed processing).
• Data Science & Analytics (DATS/INAN) Level 5: Strong understanding of analytics and machine learning techniques. While not necessarily coding models, able to guide data scientists in choosing the right methods and validate that analytical approaches align with business goals.
• Enterprise & Data Architecture (STPL & DTAN) Level 5: Proficient in aligning data architecture with overall enterprise architecture. Knowledge of data modelling and design principles (conceptual, logical, physical data models) to ensure cohesive data structures across the organization.
• Governance (GOVN) Level 5: Capability to implement governance frameworks setting up data stewardship, policy enforcement, and audit processes. Ensures compliance with data-related regulations and internal governance standards.
• Innovation (INOV) Level 5: Demonstrated innovation in leveraging emerging data technologies (e.g., AI, IoT data, cloud analytics services) for competitive advantage. Promotes new ideas and experiments in the data domain to drive continuous improvement.

Preferred Technical Competencies:

• Information Security (SCTY) Level 5: Familiarity with data security practices, encryption, and identity/access management related to safeguarding sensitive data across platforms.
• Business Intelligence (BINT) Level 5: Experience with BI and visualization, ensuring that data presentation through dashboards and reports meets the needs of decision-makers.
• Artificial Intelligence (AIDS/MLNG) Level 5: Understanding of AI/ML infrastructure (model deployment, MLOps) and algorithms, to effectively oversee integration of advanced analytics into products.
• Cloud Architecture: Strong grasp of cloud data services (e.g., Azure Synapse, AWS Redshift, Google BigQuery) and how to architect cost-effective, scalable solutions using them.

Tools, Platforms & Certifications:

• Tools/Platforms: Extensive experience with modern data tech stack: distributed data processing (Hadoop/Spark), data warehouse and lakehouse solutions, ETL/ELT tools (Azure Data Factory, Apache NiFi, etc.), and real-time streaming (Kafka, Apache Flink). Proficient in SQL and at least one programming language for data (Python, Scala). Familiar with analytics platforms (Power BI, Tableau) and ML platforms (Azure ML, TensorFlow). Also knowledgeable about data governance tools (Collibra, Informatica) for cataloging and lineage.
• Certifications: Relevant certifications such as Certified Data Management Professional (CDMP), Cloud Architect certifications (Azure Data Engineer, AWS Big Data Specialty), and any Data Science/AI certifications. Certifications in data governance or security (e.g., CIPP/E for privacy) could be valuable.

Required Behavioural Competencies:

• Strategic Influence: Excellent at articulating the value of data initiatives to business leaders and securing buy-in. Can influence peers and executives by translating technical concepts into business outcomes and demonstrating how data drives strategic objectives.
• Analytical Mindset: Naturally data-driven in decision-making. Uses evidence and metrics to guide actions and encourages the same rigor throughout the team. Comfortable diving into data details when necessary to understand issues or spot opportunities.
• Leadership & Mentoring: Proven leadership in building multidisciplinary data teams. Invested in mentoring data engineers, scientists, and analysts, and fostering a community of practice that shares knowledge and elevates skills.
• Collaboration: Works seamlessly with other departments, understanding their data needs and pain points. Facilitates an environment where data teams partner with domain experts and IT colleagues to create solutions together.
• Integrity & Compliance: Holds a strong ethical stance on data usage. Instills a mindset of responsibility regarding data privacy and compliance. Ensures the team treats data governance not as red tape, but as essential to trust and quality.
• Adaptability: In the rapidly evolving data field, remains agile and open to change. Quickly adapts strategy in response to new data regulations, technology disruptions, or shifting business priorities, while keeping the team focused and motivated.

Experience & Education Requirements:

• Experience: 12+ years of experience in data-focused roles. This should include substantial experience (5+ years) in leading data engineering or analytics teams, preferably at an enterprise level or in a large-scale data environment. A successful track record of implementing data platforms or large analytics programs is required. Experience should also show ability to interface with business leadership e.g., presenting data strategy or running data innovation workshops.
• Education: Bachelor's degree in Computer Science, Data Science, Information Systems, or related field (required). Master's or PhD in Data Science, Information Management, or MBA with a technology focus (preferred). Strong educational grounding in database systems, data architecture, and statistics/machine learning is expected.
• Additional Qualifications: Publications, conference presentations, or significant project achievements in the data & AI space will be seen as a plus, demonstrating thought leadership. Experience within industries relevant to ADQ's portfolio (such as finance analytics, healthcare data compliance, etc.) is beneficial.
  

Disclaimer:

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