Data Controller

Job description

Trading Analytics and Flow Quality

• Traffic profitability metrics: spread and commission revenue, PnL by book, and all Total PnLmetrics, broken down by instrument, region, traffic source and partner.
• Toxic flow detection: latency arbitrage, scalping, exploitation of swaps and price gaps, and coordinated client groups.
• Analytical support for A/B-book routing: which clients to route to the market and on what criteria, with measurement of the actual effect of each decision, and monitoring of the hedge position.

Partners and CPA

• A multi-level evaluation model for partners and introducing brokers (IBs).
• Detection of CPA abuse and of abuse in revenue-share payouts.
• Partner tiering and the economic rationale for the terms of each tier.

Payments and Financial Reconciliation

• Payment flow analytics: deposit conversion by payment service provider (PSP), declines, processing costs and withdrawal times.
• Detection of payment abuse: AML patterns in deposits and withdrawals, chargeback risk and mismatches between account holders and payment details.
• Reconciliation of internal records with providers and financial reporting, with automatic detection of discrepancies.
• A single source of truth for revenue, agreed with Finance.

Marketing and Client Analytics

• End-to-end funnel from lead to registration, first-time deposit (FTD), active trader and retention, with accurate attribution by channel.
• Acquisition economics: CAC, LTV and cohort payback by channel and region, and assessment of lead quality at an early stage rather than after three months.
• Analytics of the retention and conversion teams: effectiveness, workload and impact on client retention.
• Segmentation of the client base and data support for campaigns.

Compliance and AML

• Dashboards and alerting in line with regulatory requirements and internal policies.
• Analytical support for KYC and AML: client risk scoring, monitoring of suspicious patterns and preparation of data for reporting.
• Responding to regulator requests using a reproducible calculation methodology.

Platform and Data

• Ownership of the data stack: storage (Snowflake, MySQL, ClickHouse), orchestration, transformations, dbt, Dagster, BI (Tableau, Hex), and integrations with the trading platforms (MT5 and proprietary), CRM, PSPs and advertising accounts.
• A data model covering trading, clients, payments and marketing in a single consistent logic.
• Data quality: tests, data freshness SLAs and monitoring of integration failures.