Key Responsibilities

- 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.

Candidate Profile

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.

Main Tasks

- 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.

Technical Skills

- 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)

Professional Skills

- Solid 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

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📌 Financial Data Analyst - Knowledge Management (Melbourne)
🏢 Invartis Consulting
📍 Melbourne