Every decision a trading business makes is really a decision about its data.
Price, risk, exposure, P&L, the close: each one is only ever as good as the data beneath it, arriving from sources the business doesn't own and feeding systems that can't wait. Keeping that data trustworthy is a discipline in its own right. It is the one we run.
Trustworthy data is harder than it looks.
In a trading business, data is not one system or one feed. It is many sources, many meanings, and many deadlines, held to a standard where right, complete, and on time are not goals but the baseline the business runs on.
The sources you don't own keep moving.
Data arrives from many providers, in many shapes, on schedules you don't control, and formats, calendars, and feeds change underneath you. Onboarding a source and keeping it flowing through every system that depends on it is engineering, not configuration.
Quality is a question of meaning, not format.
Data can pass every format check, land on time, and still be wrong, because the same price, curve, unit, or delivery period can mean different things in two systems. Real quality is correctness in context, proven across systems, not a value that merely validates.
Trust is something you can prove.
A number the business relies on has to be traceable: to its source, its timing, the rule that shaped it, and everyone downstream who depends on it. Lineage, controls, and audit are what turn "we think it's right" into "we can show it."
The close doesn't wait.
Every position, valuation, and risk number has to converge on time. A deadline turns a data question into a business one. We engineer for correctness and availability, turning recurring issues into automation and durable fixes, so the data gets quieter over time.
The data a trading business runs on.
Trading runs on more than the trades. It runs on the prices, curves, fundamentals, and reference data feeding every position and every number downstream, received from the outside, and produced inside.
Market data & curves
Trade, position & P&L data
Fundamental, asset & meter data
Reference, static & master data
FX, rates & macro
Risk, valuation & derived data
Different markets, the same disciplines. Data that has to be right, on time, and provable, wherever it runs.
The whole data lifecycle, and where we have actually run it.
Trustworthy data is not one capability. It is a discipline at every stage, from strategy through to the close. We advise on data strategy from the run-side, because production shows which architectures, controls, and operating models actually hold under trading pressure. This is the lifecycle, honestly marked.
Strategy, ownership & criticality
- Target-state & platform design
- Critical-data identification
- Ownership & operating model
- Data SLAs · build-vs-buy
Grounded in what breaks in operations, not the diagram.
Source acquisition & ingestion
- Feed building & onboarding
- Vendor & exchange entitlements
- Batch, API, file & streaming ingestion
- Replay, backfill & late-arriving data
- Idempotent pipelines
Contracts, reference data & semantics
- Data contracts & schema evolution
- Instrument, entity & counterparty mastering
- Calendars, tenors & delivery periods
- Timezone, unit & identifier alignment
Storage, modelling & history
- Warehouse / lakehouse zones
- Time-series modelling
- Historisation & as-of dates
- Corrections & audit history that survives a restatement
Processing & pipelines
- ETL / ELT
- Python data engineering
- DAG & dependency orchestration
- Reruns, checkpointing & controlled reprocessing
- Promotion paths
Quality, reconciliation & observability
- DQ controls: completeness, timeliness, validity
- Cross-system & source-to-target reconciliation
- Break detection & tolerance bands
- Freshness & volume anomaly detection
Knowing the data is correct, not just that the pipeline is up.
Governance, lineage & access
- Entitlements & market-data licensing
- Lineage & impact analysis before change
- Classification, retention & access control
- Audit evidence & maker-checker control
DataOps, serving & consumption
- 24/7 data operations
- CI/CD for data
- Incident → automation loop
- Delivery to risk, reporting & the close
- Governed access for analytics and AI
In one European energy trading environment, we run market-data operations across 20+ applications and hundreds of sources (thousands of model runs and alerts a day) at around 99.9% operational availability. Automation has cut mean-time-to-resolution by roughly 40% and data inconsistency by around half. Accurate, on time, around the clock.
Anonymised by design. We go specific in conversation, under NDA.
AI is only as honest as the data path underneath it.
Reliable operations and credible AI both depend on the same thing: data you can prove is right: sourced, reconciled, governed, and observed in production. That is the discipline we run, and the foundation everything above it stands on.