In capital markets, speed, accuracy and auditability of data are fundamental. Trading decisions, risk calculations and regulatory reporting all depend on a reliable data foundation, yet most firms still manage fragmented market, transaction and reference data across multiple legacy systems. Financial data engineering solves this by transforming raw inputs into structured, validated and analytics-ready information streams. Our teams build secure pipelines that ingest data from exchanges, custodians, trading platforms and internal ledgers, ensuring consistency, traceability, and compliance across the full data lifecycle.

This capability supports critical capital-markets functions, including automated trade reconciliation, compliant reporting under MiFID II, EMIR, SFTR and Basel rules, and high-performance pipelines for risk and liquidity analytics. We also design modern data-lake architectures that consolidate historical ticks, order flows and derived metrics for quantitative research and AI-driven insight. The result is a data environment that delivers faster analysis, reduces operational and regulatory overhead, and provides decision-makers with clean, trusted information.

Capital markets still depend on order-management platforms, pricing engines and risk applications that were designed for a different era. These legacy environments often operate in isolated silos, relying on outdated databases and batch processes that slow down analysis, limit transparency and make regulatory compliance harder. As data volumes grow and reporting demands tighten, firms need modern, flexible architectures that can support real-time operations without disrupting critical trading activity.

Our digital finance transformation teams modernise these platforms end-to-end. We re-engineer legacy architectures, migrate workloads to secure cloud environments and integrate modern fintech tools using technologies such as AWS, Azure, Kafka, Spark, Flink and full-stack development frameworks. By combining these capabilities with deep knowledge of regulatory reporting, market-data integration and risk-system interoperability, we help firms replace fragile systems with scalable, cloud-ready infrastructure. The result is faster delivery, cleaner data and a technology stack that supports automation, AI-driven analytics and future regulatory requirements.

Trading and portfolio-management systems sit at the centre of every capital-markets operation, but many firms still depend on fragmented, legacy technology that struggles with modern demands. As volatility rises and regulatory expectations tighten, these platforms must handle real-time data, complex instrument pricing, and accurate risk and P&L reporting across multiple asset classes. Without modernisation, firms face slower decision-making, reduced transparency and higher operational risk.

Our teams modernise and extend these systems end-to-end. We upgrade OMS, EMS and back-office integrations, rebuild trade-lifecycle components, and migrate proprietary engines and portfolio databases to cloud-native, event-driven architectures. Using toolkits such as FIX, REST, WebSocket APIs, C++, Java, Python, Kafka, Redis and TimescaleDB, and with experience across platforms like Calypso, Murex, Bloomberg AIM and Aladdin, we deliver faster trade processing and clearer P&L visibility. The result is a more resilient, scalable technology stack that gives trading desks and portfolio managers the confidence to act on timely, trusted data.

Regulatory compliance in capital markets has become a real-time, data-intensive function. Firms must maintain clear audit trails, accurate data lineage and timely reporting across trading, risk, and finance systems. Yet many still depend on legacy rule engines and manual reconciliations that slow processes and increase the risk of error. As reporting obligations under MiFID II, EMIR, SFTR, Dodd-Frank and Basel III continue to expand, outdated technology makes compliance more costly and less reliable.

Our teams modernise and automate these regulatory workflows end-to-end. We design systems that capture and validate transaction data, integrate with trade repositories, upgrade RWA and capital-adequacy models, and build cloud-ready regulatory data warehouses using platforms such as AWS, Azure, Snowflake and Databricks. With automated ETL pipelines built in Python, SQL and Airflow, and connectors for DTCC, UnaVista and ARM/TR systems, we deliver faster, more resilient reporting infrastructures. The result is a compliance environment that reduces operational overhead and gives regulators and internal risk teams full confidence in data accuracy and governance.