• Diseñar arquitecturas de datos modernas, escalables y orientadas a negocio • Liderar la preventa técnica y la defensa de soluciones ante clientes • Traducir tecnología en valor (storytelling + ROI) • Ejecutar assessments, consultoría técnica y PoCs • Actuar como puente entre preventa y delivery • Explorar nuevas tendencias (GenAI, Data Stack moderno, etc.)

• Engage with stakeholders, both internal and external, to understand business requirements, provide strategic guidance, and ensure that data solutions meet their objectives • Support the design, development, and implementation of data architectures that meet the needs of our clients, ensuring alignment with security, performance, and scalability requirements • Utilise extensive experience with AWS tooling and other cloud providers to architect and deploy secure, cloud-native solutions for batch and real-time data processing • Provide technical leadership and guidance to cross-functional teams, ensuring best practices in data architecture, security, and cloud computing • Proficiency in data modelling, ETL processes, data warehousing, distributed systems and metadata systems • Utilise Apache Flink and other streaming technologies to build real-time data processing systems that handle large-scale, high-throughput data • Ensure all data solutions comply with industry standards and government regulations, maintaining the highest levels of data security and integrity • Monitor technical deliverables against the designs, manage and report on design divergences • Support the sales and pre-sales teams by providing expert advice and input on proposals, ensuring technical feasibility and alignment with client needs • Advise and support on breaches of data standards and make recommendations about how they should be resolved • Stay up to date with the latest advancements in cloud technologies, data architecture, and security practices, and apply this knowledge to continuously improve our offerings.

Role Description We are hiring a Senior Data Engineer / Architect to own the technical execution of our Golden Record platform and the broader data infrastructure that extends from it. The Golden Record is our central identity resolution system, resolving messy, overlapping client and instrument data from five source systems into canonical entities. This is not a “build dashboards” role. You will design and build: - Identity Resolution Platform: - The Golden Record — our central system for answering “who is this client?” and “what is this instrument?” - Entity registry, resolver API, and human-in-the-loop review queue for match confirmation. - Integration with external reference data sources (OpenFIGI, EDGAR, GLEIF/LEI) for automated enrichment. - Event Bus + Integration Architecture: - AWS EventBridge as the firm's enterprise event bus. - Ownership of event contracts, schema enforcement, and expansion into trade, research, CRM, and readership domains. - Data Warehouse + Analytics: - Analytical layer built on dbt — staging models, dimensional marts, and bridge tables. - BI dashboards for client coverage, trading activity, readership analytics, and CRM gap analysis. - Data quality monitoring to track resolution rates, alias coverage, and entity drift. Qualifications - 8+ years building data platforms, backend services, or data engineering infrastructure. - Deep experience with AWS serverless (Lambda, API Gateway, EventBridge, Aurora, CDK). - Strong PostgreSQL skills — schema design, query optimization, migrations. - Production experience with event-driven architecture (EventBridge, SNS/SQS, Kafka, or similar). - Hands-on data warehouse and ETL/ELT experience — designed schemas, built pipelines, and operated a warehouse in production. - dbt proficiency — staging/mart patterns, incremental models, testing, documentation. - TypeScript or Python fluency (ideally both; TypeScript is primary for infrastructure). - Experience with entity resolution, master data management, or identity matching problems. - Active use of AI-assisted development tools (Cursor, Claude Code, Copilot, or similar). - Comfort working autonomously in a small team with direct access to business stakeholders. Requirements - Familiarity with broker-dealer operations, trade lifecycle, or research distribution. - Experience with CRM systems (Tier1/S&P Global, Salesforce, or similar). - Data quality frameworks and monitoring (Great Expectations, dbt tests, custom). - Prior experience at a small firm where you owned the full stack, not just one layer. What this role is not - This is not a data science or ML role; the entity resolution logic is rule-based and human-in-the-loop. - This is not a front-end role; the primary UI is BI dashboards and a lightweight HITL review queue. - This is not a large-team management role; you'll be the primary technical executor. - This is not a “move fast and break things” environment; data accuracy matters more than speed. How we work - Small team, high trust; decisions happen in conversation, not in tickets. - Architecture-first; executing a clear vision with significant input on implementation details. - AI-native development; using LLMs and agentic tools throughout our engineering workflow. - Human-in-the-loop is core; building systems where humans are in the critical path. - Legacy respect; wrapping old systems with a clean architecture.

• Architect, design, and manage end‑to‑end Snowflake‑based data platforms , ensuring high performance, scalability, and reliability • Lead data modelling initiatives (dimensional, normalized, and hybrid models) to support complex analytical and financial reporting needs • Design and implement ETL/ELT pipelines , including orchestration, data ingestion, and real‑time or near‑real‑time streaming integrations • Collaborate with stakeholders to analyse data requirements, translating business needs into effective data architecture designs. • Ensure best practices in Snowflake implementation , including performance optimisation, security, access control, and storage management. • Develop advanced SQL and Python scripts for data transformation, automation, and validation • Integrate DevOps practices such as CI/CD and infrastructure‑as‑code into the data engineering lifecycle • Provide architectural leadership and mentoring to data engineers, fostering high standards for delivery, documentation, and collaboration • Support proofs of concept (POCs) and contribute to technical evaluations of tools and technologies