All Data Engineers should have:
•Very Strong Databricks Lakehouse principals and experience
•Hands-on Unity Catalog experience
•Experience developing Spark Declarative Pipelines (Lakeflow/DLT , autoloader)
•Strong pipeline development capabilities
•Deep Experience with DBT model development, conversion, and refactoring
•Exposure to Power BI and analytics consumption patterns
•Working knowledge of Azure DevOps CI/CD pipelines
•Familiarity with metadata-driven frameworks
•Exposure to code generation and agentic engineering patterns
•Strong SQL and PySpark development skills
Required Non-Technical Skills
Collaboration
•Ability to work closely with BI and QA teams
•Strong partnership mindset
•Ability to participate in technical design discussions
•Effective stakeholder engagement
•Routinely partners with QA to define testing criteria for data accuracy and proactively updates Project Management on technical blockers, delivery timelines, and sprint progress without requiring micromanagement.
Communication
•Excellent verbal communication skills
•Strong written communication skills
•Ability to explain technical concepts clearly
•Ability to communicate blockers and dependencies effectively
Ownership
•Strong accountability
•Ability to work independently
•Takes ownership of assigned deliverables
•Drives issues to closure
Risk Management
•Raises alarms early when risks are identified
•Escalates blockers appropriately
•Proactively identifies project dependencies
•Communicates schedule or quality risks effectively
Problem Solving
•Strong analytical skills
•Root cause analysis capabilities
•Ability to investigate data discrepancies
•Solution-oriented mindset
Critical Hiring Requirement
These roles are intended to accelerate delivery immediately and are not entry-level or development positions. Candidates must have proven, hands-on experience with Databricks, Unity Catalog, Spark Declarative Pipelines (Lakeflow/DLT), DBT development and refactoring
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