• Ensure the reliable, accurate, and timely flow of data that underpins all investment analytics, reporting, and decision-making within the business.  

• Ensure pipelines meet daily and monthly SLAs for reporting cycles. 

• Support the full data lifecycle ─ from ingestion and validation through to transformation and delivery. 

• Contribute to the design of modular, scalable data pipelines and participate in architecture discussions for new investment-data workflows. 

• The role exists to strengthen operational resilience, improve data quality, and drive the automation and optimisation of processes across the investment analytics function. 

• Support data lineage tracking, metadata management, and governance standards to ensure transparency for audits and regulatory reporting. 

• Strong attention to detail and analytical thinking. 

• Curious mindset with a willingness to learn and solve complex problems. 

• Ability to work under pressure during reporting cycles. 

• Good communication skills ─ especially when interacting with Analytics and Dev teams. 

• Proactive, organised, and able to prioritise multiple tasks. 

• Comfortable working with large datasets and technical workflows. 

• Strong interest in emerging technologies, automation & AI. 

• Curious mindset with a passion for creative problem-solving and “thinking differently.” 

• Ability to balance structured analysis with open-ended exploration. 

• Ability to document pipelines, data contracts, assumptions, and workflows to ensure reproducibility and team resilience. 

Data Pipeline Operations 

• Monitor, run, and validate daily ETL processes across pricing, benchmarks, holdings, transactions, and market data sources. 

• Troubleshoot data breaks, identify root causes, and implement timely fixes. 

• Ensure accurate loading of data into core systems (Databricks, SQL databases proprietary analytics systems). 

• Maintain documentation of data flows, ingestion processes, and data dependencies. 

Data Quality & Validation 

• Apply and enhance rules-based data quality checks. 

• Investigate anomalies raised by analytics or risk teams and escalate appropriately. 

• Partner with the Development (Dev) team to refine validate rules and data model logic. 

Cross-Team Collaboration 

• Work with investment analytics to support daily & month-end cycles, reporting deadlines, and bespoke client deliverables. 

• Partner with the development team to automate recurring tasks and improve data infrastructure. 

• Communicate clearly and proactively when issues arise that may affect downstream reporting. 

• Work with external data vendors, fund administrators and prime brokers using APIs and file-based feeds to ensure reliable ingestion of market and benchmark data. 

Automation & Continuous Improvement 

• Build scripts and tools to reduce manual work across the analytics and data operations pipeline. 

• Contribute to process re-engineering and optimisation initiatives. 

• Support the migration of legacy processes to modern data platforms (e.g., Databricks / Delta Lake). 

Domain Learning & Application 

• Develop understanding of investment data, financial instruments, portfolio holdings and trades and price sources. 

• Learn fundamentals of return calculations, risk metrics, attribution methodologies, and benchmark composition. 

• Become capable of understanding how data quality impacts reporting outcomes. 

• Gain exposure to NAV calculations, benchmark methodologies, different return types and risk metrics. 

• Collaborate with RisCura teams including the Dev team, Data Management teams and Business Analysts. 

• Build relationships with external market and portfolio data providers.