Job Description & Details
This is a heavy-duty data management gig focused on Anti-Money Laundering (AML) risk modeling rather than just building pretty dashboards. If you actually enjoy deep-diving into messy enterprise data, mapping out authoritative sources, and setting up strict data quality controls, this role gives you plenty of room to dig in.
What You'll Actually Be Doing
Expect to spend a good chunk of your time elbow-deep in source-to-target data mappings, profiling massive datasets, and figuring out where compliance data is breaking down. You'll work closely with product managers and data engineers to ensure that the data pipelines feeding the AML investigative teams are clean, reliable, and properly governed. This isn't a BI reporting role; you will be dealing with the foundational plumbing of enterprise data assets.
The Core Tech Stack
You'll need to be dangerous with Advanced SQL and Python for querying and manipulating large-scale datasets. On the infrastructure side, hands-on experience with Snowflake, Databricks, and AWS products is non-negotiable because that's where all the heavy lifting happens. They want someone who understands modern cloud data ecosystems and can collaborate smoothly with data engineering teams to ship production-ready pipelines.
Interview Expectations
Expect the hiring team to test your deep understanding of data profiling and lineage by asking you to walk through how you would track down a discrepancy in a complex, multi-source enterprise dataset. They want to see your methodical approach to finding root causes and establishing preventative controls. You'll also likely face live technical questions on advanced SQL joins, window functions, and optimizing queries in cloud data warehouses like Snowflake, where the interviewer is secretly checking if you write efficient code or just brute-force your way through data.
Application Advice
Your resume needs to make it abundantly clear that you do actual data management, not just Tableau or PowerBI reporting. Ditch the visualization fluff and highlight your concrete experience with data mapping, profiling, and quality governance. Sprinkle in heavy keywords like authoritative data sources, Snowflake, Databricks, Python, and SQL to breeze past the automated ATS filters and grab the attention of the engineering managers.