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.
● 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.
