A major financial services organisation is strengthening the data foundations behind its capital, liquidity and regulatory reporting. This is a hands-on analytical role for someone who is comfortable pulling apart reporting figures, tracing them back through system transformations to source records, and proving whether the numbers reconcile. You won't be producing regular reporting output; instead you will be interrogating how existing reporting is built, uncovering where it breaks down, and helping design and prove out fixes.
What You'll Be Doing
Data investigation and reconciliation
Pull data together from source systems, data platforms and calculation engines used across capital and prudential reporting.
Profile large, complex datasets for gaps, inconsistencies, duplication and timing issues.
Reconcile figures across source data, transformation layers, calculation outputs, the general ledger and final regulatory submissions.
Chase down anomalies, unexplained movements and failed reconciliations to their root cause.
Size the financial and reporting impact of issues you uncover, and back it up with clear, reproducible working.
Build repeatable reconciliation routines rather than one-off analysis, so the work can be run again.
Lineage, controls and testing
Map how data actually moves from source systems through to what lands in a regulatory return, including every manual step along the way.
Flag gaps in ownership, documentation or controls as you find them.
Help design data-quality checks and reconciliations that catch problems before they reach a report.
Write test cases for reporting or data changes, run the testing, and compare results against what was expected.
Log defects and open questions clearly enough that someone else could pick them up.
Working with stakeholders
Take direction from senior reporting leads on priorities and technical requirements, without needing to independently interpret prudential standards yourself.
Sit in on process walkthroughs and data workshops with finance, treasury and risk teams to understand how reporting actually gets built today.
Turn technical findings into summaries and visuals that a non-technical stakeholder can act on.
Keep organised, audit-ready working papers, assumptions and version history.
What Good Looks Like
You've handled deep reconciliation work before, not just BAU reporting production.
You are genuinely comfortable in large, messy datasets and enjoy the detective-work side of the job.
You can trace a number backward through several layers of transformation and explain, in plain English, why it changed.
You document as you go rather than reconstructing your logic after the fact.
You can hold a room of finance, risk or technology stakeholders and explain a technical finding without losing them.
Experience We're Looking For
Must have
Strong, hands-on reconciliation experience, including tracing reported figures back to underlying transactions.
Advanced Excel skills across large, complex datasets.
Experience extracting and analysing data from multiple source systems.
A solid grasp of data quality concepts: completeness, accuracy, validity, consistency and timeliness.
Experience documenting data lineage or end-to-end reporting flows.
Experience running or supporting formal testing and validation of data or reporting changes.
A track record of producing clear evidence and working papers for senior stakeholders, audit or assurance reviews.
Nice to have
SQL, or hands-on experience with relational databases, data warehouses or enterprise data platforms.
Power BI, Tableau or similar visualisation tools.
Python, R or SAS.
Exposure to regulatory reporting platforms, treasury systems or general ledger environments.
Background in prudential or capital reporting within an APRA-regulated bank or financial institution.
Experience supporting a remediation programme, regulatory review or internal audit response.
Why This Role
This sits inside a genuine reporting and controls uplift programme, not routine BAU reporting. You will get exposure across finance, treasury, risk and technology stakeholders, and the chance to shape how data quality and control work gets done, not just execute against someone else's design.
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