Senior Data Engineer

Key responsibilities:

• Architect and build scalable, reliable data pipelines and platformssoftware solutions for data products, ensuring performance, quality, and long-term sustainability.

• Own data solutions end-to-end — from translating business objectives into technical designs through implementation, deployment, and production support.

• Design and implement data workflows that are reproducible, testable, and scientifically rigorous, embedding validation frameworks, monitoring, lineage, and observability into every stage.

• Enable advanced analytics and AI/ML use cases by building software solutions infrastructure that supports experimentation, versioning, and production-grade pipeline and model deployment.

• Lead architectural decisions and influence technical prioritisation, partnering closely with product and delivery teams to align engineering effort with business impact.

• Act as a technical authority within the team, elevating engineering standards and driving best practices in data management, governance, and cloud-native development.

• Engage senior stakeholders, clearly communicating complex technical trade-offs and recommendations in business-relevant terms.

• Collaborate cross-functionally with engineers, data scientists, analysts, and product leaders to deliver high-impact solutions for institutional investors and private markets clients.

What we are looking for:

• Proven experience building and operating scalable software systems for data processing workflows and platforms, with deep expertise in Python and SQL across databases such as Snowflake and Postgres.

• Hands-on experience with modern software engineering practices, including version control (Git), CI/CD pipelines, automated testing frameworks, and containerisation and orchestration (Docker, Kubernetes).

• Experience working in cloud environments (AWS or Azure), including infrastructure provisioning and automation using Infrastructure as Code (e.g., Terraform).

• Demonstrated ability to design production-grade systems that balance performance, scalability, reliability, security, and maintainability.

• Experience enabling or supporting advanced analytics and AI/ML use cases in production environments.

• A rigorous, data-driven mindset – comfortable using analysis, benchmarking, and experimentation to guide technical decisions and architectural trade-offs.

• Strong understanding of data validation, testing strategies, and code quality practices, with confidence applying diverse code and data testing techniques across data and application layers.

• Ability to operate autonomously and drive technical solution design end-to-end, taking ownership of outcomes.

• Experience collaborating effectively across engineering, data science, product, and design teams to deliver high-impact solutions.

• Excellent written and verbal communication skills, with the ability to influence stakeholders at all levels and translate complex technical concepts into clear, business-relevant language.

• A proactive, curious, and resilient mindset — motivated to explore new technologies, tackle ambiguous problems, and continuously improve systems and ways of working.

Desirable skills include:

• Experience with AI-related technologies and products; familiarity with using AI coding assistants.

• Experience working with financial market data, investment analytics, or private markets datasets.

• Experience designing and building data platformssoftware and/or data platforms in regulated or financial services environments.

• Experience supporting ML lifecycle management (model versioning, experiment tracking, model deployment pipelines); familiarity with tools such as MLflow, feature stores, or model serving frameworks.

• Experience productionising statistical or quantitative models.