Financial Data Engineering & Analytics
Trusted financial data platforms
We build financial data pipelines, warehouses and reporting your finance team and auditors can both trust.
- Data pipelinesreconciliation-grade
- Lakehouse & warehouseone source of truth
- Real-time streamingfresh data
- Quality & lineageauditors accept it
- BI & analyticsself-serve
Why teams bring us in
The trigger is usually a number that cannot be defended in a meeting.
Reports that disagree
Your billing system, your ledger and your BI tool each report a different revenue figure for the same month. Finance keeps a spreadsheet that quietly becomes the source of truth. We trace the divergence to its source, then rebuild the path so the three agree by construction.
A close that eats a week
Month-end becomes a scramble of manual exports, chased approvals and late corrections that arrive after the numbers were already sent upward. We move the reconciliation work into the pipeline itself, with exception queues for the cases that genuinely need a human, and shorten the calendar rather than the checks.
A warehouse nobody trusts
There is a warehouse. Analysts still export to spreadsheets because they have been burned by a silently failed load. Trust returns through boring mechanics: idempotent loads, freshness checks that fail loudly, and a documented model where each field has one definition and one owner.
Ingestion and pipelines
Everything downstream inherits the discipline of how data arrives.
Change data capture from core systems
Nightly full extracts hide intraday movement and break as volumes grow. We read the transaction log instead, capturing inserts, updates and deletes from ledgers, payment processors and core banking with ordering preserved. Deletes matter here. A row that disappears from a source table without a corresponding record downstream is how a reconciliation breaks six weeks later.
Idempotent loads and exactly-once semantics
Pipelines fail mid-run. The question is what happens on the retry. We design loads that can be replayed against the same window without duplicating rows, using deterministic keys and merge logic instead of blind appends. When a payment file gets reprocessed after a network timeout, the settlement total stays correct and no one spends a morning hunting phantom transactions.
Late-arriving data and correction handling
Financial data arrives out of order. A card settlement lands three days after the authorisation, a correcting journal entry backdates into a closed period, an FX rate gets restated. We build explicit handling for this, with restatement rules, effective-dated records and reprocessing windows, so a figure published on Tuesday can be explained on Friday after the underlying data shifted.
Storage and modelling
Storage design decides whether analysts get answers or homework.
Lakehouse and warehouse foundations
We build on open table formats with ACID transactions and time travel, so raw records stay queryable alongside curated tables. Layering matters. A landing zone that preserves the source exactly, a conformed layer where entities agree, and marts shaped for how finance actually asks questions. Rebuilding a marts table from raw becomes a routine operation instead of a project.
Streaming for intraday positions
Some questions cannot wait for tonight's batch. Fraud scoring, treasury cash position, limit monitoring. We run streaming pipelines with windowed aggregations and replayable topics, then reconcile the streaming view against the batch ledger daily so the fast number and the correct number stay within a known, monitored tolerance rather than drifting apart silently.
Quality gates and lineage
Tests live in the pipeline, following dbt-style transformations with assertions on uniqueness, referential integrity and balance checks that must hold before data publishes. Column-level lineage records where each field came from and which transformations touched it. When a controller questions a figure, the answer is a lineage graph and a test history, produced in minutes.
Feature stores for models
Credit and fraud models fail in production when training features differ from serving features. We build a feature store with point-in-time correct joins, shared definitions between offline training and online inference, and versioned feature sets. A model reviewer can reconstruct exactly what the model saw on a given decision date, which is what model risk documentation requires.
Serving and governance
A number is only useful once the right people can reach it and prove where it came from.
Semantic layer and self-serve analytics
Metric definitions belong in code. Twelve dashboards each calculating net revenue their own way is how two teams end up arguing about the same quarter. We build a semantic layer where measures and dimensions are defined once and consumed by BI tools, notebooks and APIs alike. Analysts then compose questions without rewriting joins, and two teams asking about churn get the same denominator.
Row-level access and data residency
Access control follows the data model rather than sitting beside it. Row-level and column-level policies restrict entity, region and client visibility, with masking for PII in non-production environments. Where residency rules apply, we keep regional data in-region and deploy processing inside your own cloud accounts, so the data never leaves the boundary your regulator expects.
Audit-ready reporting
Reporting under ASC 606 and IFRS 15 needs more than a correct total. It needs the path to that total. We keep immutable audit trails covering source record, transformation version and publication timestamp, plus point-in-time snapshots of any report as issued. External auditors get a drill-down from the disclosed figure to the underlying transactions.
How we work
Most reporting problems are lineage problems, so that is where the work starts.
Presales engineering
We map where the data is, who owns it, what it means, and which figures are contested. Contested definitions are a governance problem and cost more than the pipelines do.
Contracts at the boundary
Producers publish to a schema. Breaking changes fail in CI rather than in someone's dashboard on Monday morning.
Quality as code
Tests, lineage and freshness checks run inside the pipeline, so a bad load is caught before it reaches a report.
Handover to the business
The point is a team that can answer its own questions without an engineer in the loop for every one.
We deploy in your accounts, under your controls
Oxagile works as your engineering vendor, building within your existing cloud, identity and security perimeter so nothing sensitive moves to ours.
What you get
Pipelines with owners and tests
Ingestion code, transformation models and their tests, documented and handed over. Each dataset has a named owner, a freshness expectation and an alerting path, so a broken load surfaces to a person before it surfaces in a board pack.
A model finance recognises
Dimensional models built around the entities your controllers already discuss, with metric definitions reviewed by the people who sign the statements. Field-level documentation explains what each column means and which source system it came from.
Lineage and audit evidence
Column-level lineage, test run history and point-in-time report snapshots, exposed through a catalogue your team and your auditors can query directly. Answering where a figure came from stops being an investigation.
Runbooks and a trained team
Operational runbooks for common failures, backfill procedures and on-call handover notes. We pair with your engineers through the build, so the system stays maintainable after our team steps back.
Where this works best
A platform is worth building when the decisions are already waiting on it.
Talk to the team who would build itGood fit
Data spread across systems, numbers two teams disagree about, and decisions held up while somebody works out which is right.
Not a fit
One system, one team, and a report that already works. We will say so rather than sell a warehouse around it.
Questions
What finance and engineering leaders usually ask us first.
Can you work with the warehouse we already have?
Usually yes. Rewriting a warehouse is rarely the right first move. We start by mapping what exists, running quality tests against current tables and finding where the disagreements originate. Often the fix is a corrected ingestion path plus a modelling layer on top. Where a platform genuinely cannot carry the workload, we say so and plan a phased migration.
How do you handle data residency requirements?
We deploy inside your cloud accounts and keep regional data in-region, including compute. Storage, processing and orchestration run within the boundary your regulator expects, and access policies are enforced in the platform rather than in application code. Where a group entity spans jurisdictions, we design per-region deployments with a governed aggregation layer that carries only the figures permitted to cross.
What makes reporting audit-ready in practice?
Three things auditors consistently ask for. Immutable trails linking a published figure to the source records behind it, versioned transformation logic so last quarter's number can be reproduced with last quarter's code, and point-in-time snapshots of reports as issued. For ASC 606 and IFRS 15 work we also model the recognition logic explicitly, with test cases covering the judgement points.
How long before we see something working?
We aim for a first production pipeline and a small set of trusted tables within the first weeks, chosen around whichever reconciliation currently hurts most. That gives your team something to check against reality early. Broader platform work then proceeds domain by domain, with each domain reaching a documented, tested state before we open the next one.
Who owns the code and the platform afterwards?
You do. Code lives in your repositories, infrastructure is defined as code in your accounts, and we hand over documentation and runbooks as part of delivery rather than at the end. Many clients keep us on for a support period or for the next domain, though that stays a choice. Nothing in the build depends on proprietary Oxagile tooling.
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AI put to work in finance operations, grounded, governed, and running in production.
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