Data Controller

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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. ● Governance: metrics glossary, data owners, access control and GDPR requirements. ● Connection graph methodology for identifying linked accounts and groups. ● The risk admin panel (an internal Data product) as a single risk-processing platform: alerts, transaction monitoring and client restrictions. Team Management ● Managing and developing the team, including hiring, grades, objectives and the bonus system. ● Quarterly planning and prioritisation, protecting the team from continuous ad hoc requests while maintaining good relationships with the business. ● Organising the team's work as a service with clear SLAs rather than a queue of individual requests.

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