Job Description & Details
This is a high-impact senior data role helping the Department of Transportation scale out their enterprise data capabilities from the ground up. You will effectively act as the pioneer for a brand-new Data Strategy & Governance function, meaning you'll need strong political and technical savvy to bridge the gap between siloed business units and hardcore engineering teams.
What You'll Actually Be Doing
Expect to spend a lot of your time talking to actual humans across various business units to figure out how they are currently breaking, defining, and using their data. You aren't just writing code in a vacuum; you are translating messy operational realities into clean enterprise standards, setting up data lineage, and ensuring that downstream reporting in Power BI doesn't rely on guesswork. You will also heavily influence how Azure Data Factory and Azure Databricks pipelines are architected for reusability.
The Core Tech Stack
Your bread and butter here will be Azure Databricks and Azure Data Factory. The team desperately needs someone who understands how to build scalable ingestion, transformation, and cleansing patterns that don't just work for one single project, but can be standardized organization-wide. If you don't intimately know how to map data lineage and design metadata inventories within a modern cloud-native Azure ecosystem, you are going to struggle here.
Interview Expectations
They are going to test your ability to handle architectural ambiguity and organizational friction. Expect a scenario-based question like: 'How do you handle two different business units that claim conflicting authoritative sources for the same core metric?' The hiring manager wants to see that you can facilitate tough cross-functional compromises without alienating technical teams. You will also likely face a deep dive into your experience optimizing Spark workloads in Databricks and designing reusable data ingestion patterns in ADF to prove your technical chops match your consulting background.
Application Advice
Make sure your resume doesn't just read like a list of ML models you've trained. You need to explicitly highlight your experience with enterprise data governance, metadata management, and cross-functional stakeholder engagement. Sprinkle in keywords like 'Azure Databricks', 'Azure Data Factory', 'data lineage', and 'authoritative data sources' so you easily clear the initial ATS scan, and use your bullet points to emphasize times you successfully bridged the gap between business stakeholders and data engineering teams.