Job Description & Details
This is a hands-on senior reporting gig for a massive financial institution where you'll take total ownership of end-to-end analytics pipelines. If you enjoy building scalable semantic models and automating data quality controls rather than just slapping charts on a dashboard, this is worth your time.
What You'll Actually Be Doing
You'll be spending your days architecting automated ETL pipelines, designing bulletproof Power BI semantic models, and setting up self-service infrastructure for business stakeholders. Expect to deal with messy enterprise data sources, writing scripts to automate testing and validation, and optimizing data lineage and governance. You'll also act as the technical anchor for the team, meaning you'll need to mentor junior devs while pushing complex deliverables across the finish line.
The Core Tech Stack
You need deep, battle-tested expertise in Power BI and advanced data modeling. Beyond clicking around the UI, you must know how to write clean Python scripts and manage version control seamlessly using GitHub. A strong grasp of ETL design patterns, automated testing frameworks, and data governance is non-negotiable since you'll be servicing a strict financial environment.
Interview Expectations
Expect to be grilled on your data modeling decisions, particularly how you handle star schemas versus snowflake schemas when dealing with massive financial datasets with complex security constraints. They'll likely ask you to whiteboard an end-to-end automation workflow for UAT and data refreshes in Power BI, so be ready to talk through failure handling and semantic model optimization. They want to see that you can troubleshoot bottlenecks independently without breaking downstream reports.
Application Advice
Make sure your resume screams data modeling and automation rather than just report building. Highlight specific projects where you took ownership of ETL pipelines, implemented governance controls, or built reusable data assets in Power BI. Drop exact keywords like semantic models, Python, GitHub, UAT automation, and data lineage right into your experience bullets so you clear the initial ATS filters without an issue.