Head of Data Architecture

The Head of Data Architecture (DA) holds the reins of data architecture decisions within the team. The Head of Data Architecture (DA) is the strategist driving the overarching data architecture strategy for the digital transformation. Working in close coordination with the business, digital, and IT teams, the Head of Data Architecture (DA) architects the data infrastructure necessary for enabling the digital and advanced AI use cases.
The Head of Data Architecture (DA) also nurtures the team's data architecture practices and mentors data engineers on specific use-cases. This role oversees the comprehensive pipeline of data flow and provides guidance to the team in managing workflows seamlessly. Through an understanding of the interdependencies of data systems, the Head of Data Architecture DA ensures the coherent organization of data across multiple platforms and its availability for key business initiatives.
KEY ACCOUNTABILITIES:

Data Architecture Design:

Lead data architecture design to support real-time event streaming, data products, and different machine learning models solving optimisation, computer vision, and LLM's problems. This involves creating blueprints for data management systems to integrate, centralize, protect, and maintain the data sources.
System Development:

Lead the design and support the implementation of large-scale data processing systems capable of handling the volume, velocity, and variety of data that needs to be analyzed and processed.
Data Strategy:

Contribute to and support EGA's data strategy, including the use of new technologies and practices, to improve the quality, reliability, and efficiency of data extraction and its use in machine learning and data product applications.
Data Modelling & Management:

Spearhead the creation and maintenance of conceptual, logical, and physical data models, supporting both operational and AI use cases. Oversee data integrity and quality assurance processes across upstream, midstream, and downstream.
Machine Learning Framework:

Lead the design of the ML Ops framework and define workflows and processes that accelerate the development and deployment of machine learning models, ensuring that ML models are production-ready and scalable.
Data Governance:

Lead and contribute to defining policies and procedures for data governance in collaboration with the data protection officer and other stakeholders, ensuring compliance with data privacy and protection laws. Lead efforts of data governance platform integration.
Collaboration:

Collaborate with IT teams, data engineers, and senior stakeholders to ensure that the data architecture supports and helps achieve strategic objectives. Lead in proposing, gaining buy-in, and driving the end solution.
Communication:

Effectively communicate complex data concepts, solutions, and proposals in simple language to non-technical team members and senior leaders (Senior Director, C-level, Executive Committee).
Mentorship and Leadership:

Act as a mentor to data engineers (4-8), providing guidance and support in their professional development. Promote a culture of performance, collaboration, and continuous learning within the data team.
Innovation and Continuous Improvement:

Stay up-to-date with industry trends and new technologies. Continuously explore innovative solutions and enhancements to the existing data architecture to improve its scalability, reliability, and efficiency. Champion and drive initiatives like event sourcing, data contracts & schema registries, data lineage, and automation downstream.
Problem Solving:

Drive solutions, anticipate, and contribute to resolving technical issues before they become roadblocks, maintaining data flow continuity and ensuring high data quality and integrity, e.g., alerting & monitoring strategies, change request frameworks, automation, scaling, and data partitioning strategies.
Project Management:

Coordinate and manage data-related projects (15-20 use cases every 3 months), ensuring timely and within-budget delivery, meeting or exceeding stakeholder expectations.
AUTHORITY/DECISION-MAKING:

Design authority on data architecture, governance, and technology selection to support scale-up ambitions of 50-60 use cases a year.
Validate data security protocols and resource allocation decisions based on use-case requirements.
Validate technical design and solutions to support 10 digital capabilities based on Industry 4.0 principles.
QUALIFICATIONS & SKILLS:

Domain Expertise
Bachelor's degree required; MS or PhD preferred.
Bachelor's in Data Science, Computer Science, Engineering, Statistics, with 10+ years of relevant experience.
Minimum 10+ years of data architecture experience with strong leadership skills, including mentoring, communication, decision-making, and project management.
Expertise in Big Data platforms and tools such as Python, Spark, NoSQL, Key-Value stores.
Hands-on experience with batch and real-time processing frameworks (Apache Kafka, Apache Spark, etc.).
Design experience with data pipelines and workflow management tools like Airflow, NiFi, or Luigi.
Knowledge of machine learning platforms like TensorFlow, PyTorch, scikit-learn.
Experience with data frameworks suitable for LLMs.
Experience designing and supporting production Cloud/DevOps environments, Data Lake, ETL/ELT jobs, especially within MS/Azure ecosystems.
Strong technical skills across hardware, software, systems, and solutions development.
Proven ability to use quantitative analysis to impact business decisions.
Understanding of software development methodologies, statistical applications, and Agile lifecycle.
Excellent stakeholder management, communication, and leadership skills.
Knowledge of data modeling, metadata management, and governance.
Ability to anticipate problems, develop solutions, and promote best practices in data engineering.
Agile/Digital Experience
Experience in Agile Development, with data architect/engineer background preferred.
Understanding of roles like Product Owner, Data Scientist, Designer within Agile teams.
Individual Skills
Exceptional problem-solving and analytical skills.
Ability to influence stakeholders at all levels.
Strong leadership and networking skills, with a focus on team development.
Excellent communication and presentation skills.
Active coaching and mentoring mindset.
Passion for data strategy and inspiring organizational data-driven culture.
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