Tech Lead

at  Lab49

Lab49 is hiring a Tech Lead for an exciting opportunity in one of the world’s most dynamic business hubs. Advance your career with a unique opportunity and become part of our pioneering team, setting new benchmarks for innovation and growth. Seize the moment to fuel your career and apply today.
Lab49 is looking for a Technical Lead to work with our clients to guide the analysis, design and build of the new banking solutions applied to their business.
This person will work with clients and teams of architects, data scientists, engineers, and visualization experts to discover and understand opportunities with clients’ data, and to present learnings back to stakeholders to create actionable insights.
The role will require a strong combination of experience successfully delivering data and analytics initiatives, financial domain expertise, and stakeholder management skills to lead engagements that create substantial value for our clients while also being commercially successful.

REQUIRED SKILLS AND EXPERIENCE:

• Detailed understanding of modern solution architecture, accounting for distributed systems, real-time messaging, data, and security and resiliency concerns
• Ability to lead and direct a team of engineers.
• Experience in large scale integration with exposure to protocols such as ISO20022 as well as modern APIs using REST, gRPC and GraphQL.
• Knowledge of NoSQL and SQL database technologies
• Necessary understanding of tools and techniques across data governance and data management capabilities such as data integration, data quality, etc.
• Demonstrable experience in building large concurrent applications
• Experience working in a modern environment, utilising both Continuous Integration and Continuous Deployment (CI/CD) and working with Microservices architectures
• DevOps tech including Docker, Kubernetes, Terraform or Pulumi, Hashicorp tools and GitHub/GitLab.
• Cloud architecture and development experience (Azure or AWS preferred)
• Commercial engineering experience is a must with languages including Java, C#, Python, C++, Kotlin, Go, JavaScript/Typescript or similar, highly regarded.
• Prior hands-on, technical experience (professionally or academically) in computer science, mathematics, business analytics, or other related disciplines
• Demonstrable knowledge of the financial services domain, ideally in multiple areas and across different asset classes
• Effective communication skills, and advanced skills using productivity software such as Excel and PowerPoint to analyse and present information
• Strong stakeholder management and interpersonal skills, including the ability to collaborate closely with both colleagues and clients in a fluid environment
• Work closely with business stakeholders and clearly communicate technical considerations

DESIRED SKILLS:

• Experience with data management and governance frameworks used in industry
• Experience with different data modelling techniques and tools, such as Concept Mapping, Domain-Driven Design, UML, Taxonomies, Ontologies, etc.
• Experience with data science and machine learning techniques and frameworks
• Experience with large cloud and big data technologies and initiatives
• Experience demonstrating technical concepts, including presenting and whiteboarding
• Own a functional deliverable throughout the complete development lifecycle.
• Agile development methodology (Scrum and its variants)
• A passion for learning new business domains and the ability to assimilate new information quickly to act as a guide for non-domain experts and technologists
• Experience in consulting, ideally with exposure to the sales and business model

• Technical Vision and Strategy: Develop and communicate a clear technical vision and strategy for data management and processing within the organization. Lead the architectural design and evolution of data systems to support our growth and innovation goals.
• Advanced Data Architecture Design: Design and implement sophisticated data architectures that support analytics at scale, utilizing platforms like Snowflake for data warehousing and Alation for data cataloguing to facilitate data governance and metadata management.
• Development and Optimization: Lead the development and continuous refinement of data processing pipelines, employing tools like Apache Kafka for messaging and event streaming to enable real-time data ingestion and processing.
• Real-Time Data Solutions: Architect and implement real-time caching and in-memory data solutions using technologies like Redis to enhance application performance and user experience through faster data access and processing.
• Analytics Platform Management: Oversee the integration and effective use of data analytics platforms such as Power BI, ensuring that data insights are accessible and actionable across the organization.
• Software Development Leadership: Maintain hands-on involvement in software developmentincluding Java, Spring, setting best practices, and ensuring high standards of quality. Mentor and guide the development team in implementing complex data processing logic, algorithms, and efficient coding techniques.
• Architecture and System Design: Craft robust, scalable, and flexible data architectures that support the needs of the business while accommodating future growth and technologies. This includes designing data models, databases, and data integration processes.
• Technical Innovation: Identify and evaluate new technologies, tools, and practices that can enhance the organization’s data capabilities. Lead the adoption of cutting-edge data technologies, such as big data processing frameworks, machine learning platforms, and cloud-based data solutions.
• Collaboration and Influence: Work closely with product owners, data specialists, and cross-functional teams to align technical solutions with business needs. Influence decision-making at the highest levels of the company with your technical expertise and vision.
• Performance and Scalability Optimization: Ensure data systems are optimized for performance and scalability. Conduct code reviews, system simulations, and performance testing to identify bottlenecks and improvement opportunities.
• Data Governance and Compliance: Collaborate with data governance teams to ensure data architecture complies with legal, regulatory, and security requirements. Promote data best practices and standards across the organization.