Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Director of Data Platforms responsible for the strategic direction, development, and management of data systems and ensuring AI readiness, supporting Open Banking initiatives, and leveraging Microsoft technologies. - DIREC005346

S.i. Systems
London
1 year ago
Applications closed

Related Jobs

View all jobs

Director Data Science

Executive Director / Principal Data Scientist

Executive Director / Principal Machine Learning Engineer

Staff Data Scientist (UK)

Associate Director, AI Data Scientist

Staff Data Scientist (UK)

Our Financial Services Client is seeking aDirector of Data Platforms responsible for the strategic direction, development, and management of data systems and ensuring AI readiness, supporting Open Banking initiatives, and leveraging Microsoft technologies. -DIREC

This is a Permanent Opportunity, Remote - Preferred Candidate location: British Columbia, open to Ontario and Alberta.

Must Have:

10+ years of experience indata management, with at least5 years in a Director/Leadership role.Proven experience indata architecture, data governance, and data platform management.  Strong knowledge of data technologies such asSQL, NoSQL, big data platforms, cloud data services (e.g., AWS, Azure), and data visualization tools.Expertise in Microsoft technologies, includingAzure Synapseand Microsoft Fabric. Experience with AI and machine learning technologies and methodologies. Knowledge of Open Banking standards and regulations. Excellent leadership, communication, and interpersonal skills. Ability to translate business needs into technical requirements and Agile solutions. Strong project management skills with experience in Agile methodologies. Bachelor’s degree in computer science, Information Technology, Data Science, or a related field;

Nice to Have:

Master’s degree Familiarity with financial services industry and regulatory requirements is an asset.

Responsibilities:

Develop and execute a comprehensive data platform strategy aligned with business objectives. Ensure the data platforms support data-driven decision-making and innovation across the organization. Stay abreast of industry trends and emerging technologies to continuously improve data capabilities. Build and scale a high-performing cross-functional team of data engineers, architects, and analysts. Foster a collaborative and innovative team culture focused on continuous improvement, agility, and excellence. Promote a culture of accountability, transparency, and empowerment within the team. Provide ongoing mentorship and professional development opportunities to enhance team capabilities. Identify and implement modern data technologies and tools to improve the team's efficiency and effectiveness. Drive the adoption of best practices and cutting-edge methodologies in data engineering, analytics, and data science. Encourage a learning and growth mindset, ensuring the team stays current with the latest industry trends and advancements. Develop and implement strategies to integrate AI and machine learning capabilities into data platforms. Ensure the team is equipped with the skills and tools necessary to leverage AI for predictive analytics, automation, and enhanced decision-making. Collaborate with data scientists and other stakeholders to identify and execute AI-driven initiatives. Lead the development and implementation of data strategies to support Open Banking initiatives. Ensure data platforms are compliant with Open Banking standards and regulations. Collaborate with internal and external stakeholders to facilitate secure and seamless data sharing. Leverage Microsoft technologies, including Azure Synapse and Microsoft Fabric, to enhance data platform capabilities. Implement and manage data solutions using Azure Synapse for big data analytics and data warehousing. Utilize Microsoft Fabric to create scalable, high-performance data fabrics that support advanced analytics and reporting. Ensure seamless integration of Microsoft technologies with existing data platforms and workflows. Oversee the design, implementation, and maintenance of scalable and secure data architectures. Establish and enforce data governance policies, ensuring data quality, integrity, and compliance with regulatory requirements. Work with IT and security teams to ensure data platforms are secure and resilient. Collaborate with business units to understand their data needs and provide Agile solutions that drive business value. Communicate data strategy, progress, and results to senior leadership and other stakeholders. Act as a liaison between technical teams and business units to ensure alignment and effective data utilization. Manage projects related to data platform development and enhancements, ensuring they are delivered on time, within scope, and within budget. Utilize Agile methodologies (e.g., Scrum, Kanban) to track progress, manage risks, and report on outcomes. Facilitate Agile ceremonies such as sprint planning, daily stand-ups, sprint reviews, and retrospectives. Ensure the data platforms are operating efficiently and effectively, with minimal downtime. Implement best practices for data management, including data integration, ETL processes, and data warehousing. Optimize data storage, retrieval, and processing to support business analytics and reporting needs.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

AI Recruitment Trends 2025 (UK): What Job Seekers Must Know About Today’s Hiring Process

Summary: UK AI hiring has shifted from titles & puzzle rounds to skills, portfolios, evals, safety, governance & measurable business impact. This guide explains what’s changed, what to expect in interviews, and how to prepare—especially for LLM application, MLOps/platform, data science, AI product & safety roles. Who this is for: AI/ML engineers, LLM engineers, data scientists, MLOps/platform engineers, AI product managers, applied researchers & safety/governance specialists targeting roles in the UK.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.

AI Team Structures Explained: Who Does What in a Modern AI Department

Artificial Intelligence (AI) and Machine Learning (ML) are no longer confined to research labs and tech giants. In the UK, organisations from healthcare and finance to retail and logistics are adopting AI to solve problems, automate processes, and create new products. With this growth comes the need for well-structured teams. But what does an AI department actually look like? Who does what? And how do all the moving parts come together to deliver business value? In this guide, we’ll explain modern AI team structures, break down the responsibilities of each role, explore how teams differ in startups versus enterprises, and highlight what UK employers are looking for. Whether you’re an applicant or an employer, this article will help you understand the anatomy of a successful AI department.