Financial Data Analyst - Knowledge Management

• Perform day‑to‑day data analysis and validation activities across the client’s data ecosystem.
• Analyse, reconcile, and validate investment and operational data to identify trends, anomalies, and data quality issues.
• Track, investigate, and resolve data exceptions, escalating issues where required.
• Support ongoing data feeds and integrations across platforms such as Aladdin, FactSet, custodians, and internal reporting tools.
• Produce recurring and ad‑hoc reports, dashboards, and analytical outputs for internal teams and client stakeholders.
• Support change initiatives by validating data impacts and assisting with user testing and post‑implementation checks.
• Collaborate with vendors and internal teams to improve data quality, timeliness, and consistency.

The successful candidate will have:

• 2 - 6 years of experience in data analysis, reporting, or data operations, ideally within investment management, financial services, or consulting environments.
• Strong experience working with structured datasets, including performance, reference data, benchmarks, and portfolio/account data.
• Hands‑on experience with analytical tools such as SQL, Python, Excel, and BI platforms (e.g. Power BI, Tableau).
• Exposure to investment management systems such as BlackRock Aladdin, FactSet, Axioma, or similar platforms.
• Demonstrated ability to perform data reconciliation, exception analysis, and root‑cause investigation.
• Strong attention to detail and a commitment to data accuracy and quality.
• Ability to communicate analytical findings clearly to non‑technical stakeholders.
• Experience working in multi‑stakeholder environments with internal teams, vendors, and clients.

• Analyse and reconcile data across upstream and downstream systems, identifying discrepancies and trends.
• Support data mastering activities, including security reference data, benchmarks, FX rates, and portfolio structures.
• Assist with performance and attribution data validation and reporting.
• Investigate data exceptions and contribute to root‑cause analysis and remediation documentation.
• Build and maintain dashboards, reports, and automated analytical outputs using SQL, Python, and BI tools.
• Support continuous improvement initiatives, including process optimisation and automation of manual data tasks.
• Contribute to documentation, SOPs, and data quality metrics reporting.
• Data Analysis – extracting, transforming, and analysing data to produce insights.
• Data Quality & Reconciliation – validating completeness, accuracy, and consistency of data.
• Reporting & Visualisation – building dashboards and reports for operational and management use.
• Investment Data Knowledge – understanding securities, portfolios, benchmarks, and performance data.
• Process Improvement – identifying inefficiencies and supporting automation initiatives.
• Stakeholder Communication – explaining analytical outcomes clearly and concisely.
• Collaboration – working effectively across technology, operations, and client teams.

• SQL (advanced querying and data validation)
• Python (data analysis, automation, scripting)
• Excel (advanced formulas, pivots, data modelling)
• BI tools (Power BI, Tableau, or similar)
• Exposure to investment management platforms (e.g. Aladdin, FactSet, Axioma)

• Strong analytical and problem‑solving capability
• High attention to detail
• Clear written and verbal communication
• Ability to manage multiple priorities and deadlines
• Continuous learning mindset, particularly around data tools and investment systems