Financial Data Analyst - Knowledge Management

Invartis Consulting; Melbourne; On-site; AUD 80,000 - 100,000; Full time

Job summary

Invartis Consulting is seeking a Data Analyst to perform day-to-day data analysis and validation activities across various platforms. The ideal candidate will bring 2-6 years of experience, particularly in investment management, and strong expertise in SQL, Python, and BI tools.

Responsibilities include data reconciliation, supporting data quality initiatives, and collaborating with stakeholders to enhance data accuracy. This role is crucial for producing analytical outputs and ensuring data integrity.

Qualifications

• 2-6 years of experience in data analysis, reporting, or data operations.
• Strong experience with structured datasets.
• Hands-on experience with SQL, Python, and BI platforms.

Responsibilities

• Perform day-to-day data analysis and validation activities.
• Analyse, reconcile, and validate investment and operational data.
• Support ongoing data feeds and integrations across platforms.

Skills

Data analysis

SQL

Python

Excel

Power BI

Tableau

Data reconciliation

Attention to detail

Problem-solving

Tools

SQL

Python

Excel

Power BI

Tableau

Job description

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

• 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