Technical skills needed for data and operations work in a CFD brokerage

Posted by SeekingHelpAndHope@reddit | Python | View on Reddit | 2 comments

I work in a CFD brokerage where my responsibilities include reviewing client account data, checking trading activity, monitoring payment information, validating KYC progress and preparing operational reports. Much of the role involves maintaining consistency between the CRM, the trading platform and external payment systems.

I would like to strengthen my technical base, especially in Python and SQL. My goal is to automate routine checks, analyze trading activity with greater accuracy and design clearer internal reports.

I am interested in understanding which specific areas of Python and SQL are most valuable for this type of environment. For example, Python tools for data processing, log analysis and scheduled tasks, and SQL techniques that help with data validation, pattern identification, time-based comparisons and cross-table consistency checks.

If you have experience in brokerage operations, risk, compliance or financial data work, I would appreciate guidance on which skills to prioritize and how they support day-to-day workflows.

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t_spray05@reddit

https://discord.gg/F7H36DTE https://www.linkedin.com/in/akshatpant3/

Love your passion and self-reflection!! ♥️

I think I have something for you. I'm working on a tool. I'm looking to collab with a simple/advanced software/data engineer, but is passionate to build something soon.

I have been work 4-6 hrs a day (besides my full time job)

I'm designing an unseemingly connected Behavioral Algo tool.

SpareServe1019@reddit

Focus on SQL window functions, constraints/indexes, and Python with pandas plus validation to automate reconciliations and reports.

SQL: get great at window functions (lag/lead, partitions), conditional aggregates, and CTEs. Build daily “diff” queries: unmatched deposits vs CRM, trades without KYC, orphan balances, duplicate payments. Add unique keys, foreign keys, and check constraints so bad records can’t land; use UTC everywhere and test DST edge cases. For timelines, bucket by minute/hour/day and compare against rolling windows.

Python: pandas + SQLAlchemy for extracts, Pandera or Great Expectations for schema/logic checks, and Prefect or Airflow to schedule. Make jobs idempotent (safe re-runs), log every step (structlog), and write small HTML/CSV reports to a shared location. For anomaly flags, use simple z-scores or rolling quantiles on PnL, slippage, and payment retries. Keep reads from a replica/warehouse and give the job a read-only role.

I’ve used Hasura for instant GraphQL on Postgres and PostgREST for quick read-only APIs; when I needed secure REST across Snowflake and SQL Server with simple RBAC and scripting, DreamFactory helped.

Nail window functions, solid constraints, and a small Python pipeline with validation and scheduling to make daily ops faster and cleaner.