Data Engineer – Trading & Client Data

Role purpose

Build trusted data pipelines and data products that support AI, trading intelligence, risk analysis and operational reporting. Bring together trading, client, payment and market data with reliable identifiers, timestamps and business definitions. Ensure analysts and models use accurate, permission-controlled and reproducible data across research and production.

Key responsibilities

Data ingestion and modelling

• Ingest orders, deals, positions, balances, equity, margin, prices and execution events from trading platforms and related systems through supported interfaces.
• Integrate CRM, onboarding, account, deposit, withdrawal, payment, support and acquisition data into governed client and account datasets.
• Build batch and streaming pipelines with incremental processing, change capture and replay capabilities appropriate to source-system constraints.
• Design data models that distinguish clients, trading accounts, legal entities, instruments, orders, executions and positions; manage historical changes and identifier mappings.

Accuracy and analytical readiness

• Normalise server time, time zones, trading sessions, currencies, contract sizes and symbol mappings so cross-platform analyses remain comparable.
• Reconcile trading and cash-flow datasets against source systems, distinguishing deposits and withdrawals from trading profit and loss and other account adjustments.
• Create point-in-time datasets and reusable feature inputs that prevent future information from entering model training or historical analysis.
• Implement checks for missing events, duplicate records, late data, outliers and schema changes, with accountable owners and remediation procedures.

Governance and operation

• Maintain data contracts, lineage, catalogues and clear definitions for metrics used in client profiling, execution quality, exposure and financial reporting.
• Apply access controls, encryption, masking, retention and deletion requirements to sensitive client and trading information in coordination with Security and Compliance.
• Optimise storage, query performance and processing costs while supporting reproducible backfills and reliable recovery.
• Work with quant, AI and reporting teams to deliver documented data products; monitor pipeline service levels and investigate data incidents through to resolution.

Essential experience and qualifications

• 5+ years in data engineering with strong SQL and Python and experience maintaining production data pipelines.
• Experience with trading, brokerage or capital-markets data and an understanding of orders, executions, positions, prices and account cash flows.
• Experience with data warehouses or lakehouses, orchestration, dimensional or domain modelling and automated data-quality controls.
• Ability to work with time-series and event data, including late arrivals, changing records, timestamp alignment and historical reconstruction.
• Experience building reliable incremental pipelines and reconciling data across heterogeneous systems.
• Understanding of data security, lineage, access control and point-in-time correctness for machine learning.
• Bachelor's degree in Computer Science, Engineering, Mathematics or a related field, or equivalent relevant experience.

Preferred experience

• Direct experience with MT4/MT5 data structures, bridge or liquidity-provider data and brokerage CRM systems.
• Experience with Kafka, Spark, dbt, Airflow, Dagster or comparable technologies suited to the existing stack.
• Experience supporting feature stores, tick-data analysis and multi-entity financial services reporting.

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