Senior .NET engineer/Python engineer

• Design and build production backend services and APIs in .NET/C# and/or Python that expose enterprise data and capabilities to internal applications and AI agents.
• Build the data access and integration layer connecting AI-enabled applications with databases, reports, analytical platforms, file systems and other enterprise data sources.
• Develop agent-consumable APIs and tools that allow AI agents to discover, retrieve and interpret enterprise data in a controlled and reliable way.
• Build and enhance data catalogue and metadata capabilities, enabling datasets, reports, fields and other data assets to be discovered through consistent APIs and semantic metadata.
• Implement data ingestion and transformation pipelines for structured and semi-structured sources, including validation, normalization, enrichment and metadata extraction.
• Build automated data-quality controls covering completeness, freshness, duplication, schema consistency, missing data and other issues that could affect downstream AI applications.
• Implement provenance and traceability mechanisms, ensuring data returned to applications and AI agents can be traced back to its authoritative source.
• Build entitlement-aware data services, ensuring users and AI agents can access only the datasets and information they are authorized to consume.
• Develop semantic and analytical services that help AI applications understand relationships between datasets, business terminology, metadata and analytical information.
• Integrate with the client’s existing AI and data platforms, following established architecture, security, governance and operational standards.
• Design solutions for large-scale enterprise and on-premise data estates, considering performance, scalability, resilience and maintainability.
• Implement automated testing and quality gates, including unit, integration, API and regression testing for production services and data pipelines.
• Implement monitoring and observability for services and pipelines, including logging, metrics, error handling and operational dashboards.
• Work closely with AI engineers, data engineers, platform teams, architects and business stakeholders to convert business requirements into robust technical capabilities.
• Participate in solution design, technical reviews and architectural discussions, identifying opportunities to simplify integrations and create reusable platform capabilities.
• Support production readiness and continuous improvement of services, including performance optimisation, troubleshooting and technical debt reduction.

• 5+ years of production software engineering experience with strong hands-on expertise in .NET/C# and/or Python, building enterprise-grade backend services, APIs and data-processing solutions.
• Strong experience designing and developing production APIs and backend services, including REST APIs, service integrations, asynchronous processing and distributed systems.
• Hands-on experience with data-intensive applications and data engineering, including ingestion, transformation, validation and processing of structured and semi-structured data.
• Strong SQL skills and practical experience working with relational databases and large enterprise datasets.
• Experience designing and implementing data access and integration layers across heterogeneous enterprise sources such as databases, APIs, file systems, data warehouses and analytical platforms.
• Experience building metadata-driven services, catalogue integrations or discovery capabilities that allow applications to find and interpret datasets, reports, fields and other enterprise data assets.
• Understanding of data modelling, metadata, data lineage, provenance and data quality concepts.
• Experience implementing data validation and quality controls, including completeness, freshness, schema validation, duplication and consistency checks.
• Experience designing secure and governed data access, including authentication, authorization, entitlements and permission-aware APIs.
• Strong understanding of software engineering best practices: clean architecture, SOLID principles, automated testing, CI/CD, code review, observability and production support.
• Experience working with cloud and/or on-premise enterprise environments and integrating solutions with existing platforms rather than creating isolated services.
• Comfortable using AI-assisted development tools and working in an environment where AI agents and LLM-based applications consume the services being built.
• Understanding of how LLM/agentic systems interact with enterprise data and tools, including tool/function calling, APIs, retrieval, and context management.
• Ability to translate business and data requirements into scalable technical solutions and work directly with architects, data engineers, platform engineers and business stakeholders.
• Fluent English and ability to operate independently within a client-facing, distributed engineering team.

*Nice to have *

• Hands-on experience with both .NET/C# and Python.
• Experience with Snowflake, Databricks, SQL Server, PostgreSQL or similar enterprise data platforms.
• Experience building services consumed by AI agents, MCP servers or other agent/tool integration frameworks.
• Experience with data catalogues, semantic layers, data dictionaries, metadata management or data governance platforms.
• Knowledge of embeddings, vector search, semantic search or retrieval-augmented generation (RAG).
• Experience with event-driven architectures, messaging platforms and background processing.
• Experience implementing fine-grained access control or entitlement models for enterprise data.
• Knowledge of financial markets, investment data, fund reporting or reference data.
• Previous experience in financial services or another regulated environment.