Job Description & Details
This is a hands-on data engineering role embedded in M&T Bank's Finance and Fin Crime pods, where you will deal with the unglamorous but critical side of enterprise data: untangling legacy mainframe files and migrating them into a modern Snowflake setup. If you enjoy modernizing massive data pipelines while dealing with strict regulatory and banking constraints, this gig offers a solid challenge without the typical startup chaos.
What You'll Actually Be Doing
Your days will be split between keeping the current lights on and migrating legacy pipelines forward. You'll maintain an existing IPC job that pulls data into Snowflake while figuring out how to translate EBCDIC-encoded mainframe files into clean ASCII. A big chunk of your time will go toward re-engineering these workflows into a medallion architecture (bronze, silver, gold layers) using Butler and SPICE, which means you'll be writing optimized Snowflake queries, managing data cleansing rules, and dealing with real-world exceptions before they hit downstream reporting.
The Core Tech Stack
You absolutely must know Snowflake inside and out—we're talking about performance tuning, warehouse sizing, data loading, and role-based access control, not just basic SQL. Alongside Snowflake, deep Informatica experience for ETL and Informatica Data Quality (IDQ) for validation is non-negotiable, especially since you'll be leaning heavily on Address Doctor. Add in some Python or Shell scripting for automation, the ability to read mainframe copybooks, and a solid understanding of medallion architecture patterns, and you'll have the exact toolset needed to survive and thrive here.
Interview Expectations
Expect the hiring team to grill you on how you handle legacy data ingestion issues, specifically around converting fixed-width EBCDIC formats and dealing with silent encoding corruptions. They want to see how you troubleshoot production failures under pressure, so be ready to walk through a time you tracked down a stubborn data quality exception using IDQ. The interviewers will also test your Snowflake optimization chops by asking how you diagnose an expensive, runaway query and restructure it for cost efficiency.
Application Advice
Make sure your resume doesn't just list tools, but explicitly highlights your end-to-end migration experience, especially if you've moved legacy systems into cloud data warehouses like Snowflake. Drop keywords like EBCDIC to ASCII conversion, Informatica Data Quality, Address Doctor, and medallion architecture right near the top of your experience bullets to clear the initial ATS filters. Emphasize your background in regulated industries or banking so they know you understand the gravity of compliance and data governance.