Interviewer Guide
Interviewer Responsibilities
Round 1 Deliverables
Establish technical baseline, assess conceptual clarity, document detailed strengths/weaknesses, and identify Round 2 focus areas.
Round 2 Deliverables
Validate application of skills, probe deeper into key technologies, assess delivery readiness.
Round 3 Deliverables
Confirm strategic fit, leadership, and communication alignment; finalize hiring decision.
Feedback & Hand-Off Expectations
- Structured skills matrix and qualitative feedback
- Clear next round focus guidance
- Recommendation summary
- Each round builds upon the previous one to ensure progressive depth and non-redundant interviews
Core Principles
Consistency: same framework, same evaluation lens
Progression: each round goes deeper
Transparency: factual, constructive feedback
Calibration: level candidates objectively (L11–L7)
Holism: assessing both engineering and delivery capabilities
Data Engineer
Round 1: Technical Assessment
Objective
Evaluate fundamental technical understanding without dependency on any specific platform or tool. This stage identifies what the candidate truly knows, not just what they have used. Focus areas and interview questions may be tailored to specific roles and requirements.
Conceptual Understanding
- End-to-end data lifecycle comprehension
- Data modeling and architecture thinking
- Batch vs. streaming vs. micro-batch data processing, ingestion and orchestration
- Data quality, governance, lineage awareness
Technical Foundations
- Programming languages (Python, PySpark, SQL, etc.)
- Distributed processing (Spark internals, partitioning, optimization)
- Event streaming (Kafka basics, message ordering, schema handling)
- Orchestration principles
Evaluation Goals
- Identify core technical strengths and weaknesses
- Determine platform familiarity (Azure, AWS, GCP, Databricks, etc.)
- Determine deepest knowledge within the data lifecycle
- Understand engineering maturity level (Junior → Senior)
Expected Output (for Round 2 Preparation)
- Primary programming language(s) and proficiency
- Tools and frameworks the candidate is familiar with
- Cloud exposure (Azure, AWS, GCP, Databricks)
- Lifecycle expertise
- Strengths and Weaknesses summary
- Recommended focus areas for Round 2
Round 2: Scenario-Based Deep Dive
Objective
Dive deeper into the candidate's technical skills by assessing proficiency with specific platforms, tools, and technologies identified in Round 1. Validate how they apply skills in real-world projects, handle delivery challenges, and approach architectural decisions.
Applied Experience
- Project examples (data volume, data type, role, architecture concepts)
- Decision-making and trade-offs (why certain tools or patterns were chosen)
Scenario-Based Problem Solving
- Debugging and incident response
- Late data arrival handling, schema drift, reprocessing logic
- Pipeline automation, CI/CD, and monitoring
Platform & Tool Mastery
- Deep dive into platform(s) identified in Round 1
- Hands-on fluency in key tools (Databricks, ADF, Glue, Kafka, Terraform, etc.)
Behavioral & Delivery Awareness
- Communication clarity
- Ownership mindset and team collaboration
- Coaching or mentoring ability
Expected Output
- Validated practical and delivery-level maturity
- Clear mapping of platform/tool strengths
- Assessment of troubleshooting skills and project ownership
- Recommendation for seniority level (L11–L7) and growth trajectory
Round 3: Executive / MD Review
Objective
Confirm organizational alignment, leadership presence, and communication maturity.
Inputs
- Consolidated feedback from Rounds 1 and 2
- Skills, strengths, and weaknesses
- Platform and delivery maturity
- Role and level recommendation
Focus
- Cultural and practice fit
- Strategic thinking and stakeholder management
- Long-term potential for client-facing or leadership roles
Data Governance
Standardized Interview Questions
Data Governance & Microsoft Purview — standardized question bank organized by category.
- Can you explain the key features and capabilities of Microsoft Purview and how they support enterprise-wide data governance?
- What is your experience with deploying Microsoft Purview for enterprise data governance?
- How would you approach designing a metadata strategy for a client using Microsoft Purview?
- What are the key considerations when implementing a data catalog solution for a hybrid environment?
- What data sources and data platforms have you registered and scanned using Microsoft Purview?
- What challenges have you faced when registering and scanning data sources in Purview?
- How do you configure a self-hosted integration runtime (SHIR) for Purview scans?
- How do you handle firewall or network restrictions when connecting Purview to private or on-premise data sources?
- What are the common causes of scan failures in Purview, and how do you troubleshoot them?
- How does Purview handle schema drift or structural changes in registered data sources?
- Provide an example of custom data classifications you have created and why they were needed.
- How has data classification been used by your customers or organization?
- How do you approach data lineage implementation in Purview?
- What limitations have you encountered with lineage in Purview, and how did you address them?
- How do you work with sensitivity labels in a data governance implementation?
- What is your experience with implementing data quality as part of a data governance program?
- How do you define and measure data quality dimensions?
- How do data classification and data quality work together in your governance approach?
- How do you design governance domains in an enterprise data governance program?
- How do you design and govern data products?
- What principles do you follow when defining data products?
- How do governance domains and data products align with business ownership and access control?
- Can you walk us through a time when you designed and implemented a data governance framework?
- What challenges did you face during implementation, and how did you overcome them?
- How do you operationalize data governance in an organization?
- What steps would you take to operationalize Microsoft Purview across the enterprise?
- How do you ensure data governance strategies align with global compliance frameworks such as OSFI, PIPEDA, GDPR, and ISO standards?
- How do you ensure compliance with global data protection regulations when designing data governance solutions?
- How do you manage access control and role-based permissions within a governance program?
- How do you communicate complex data governance concepts to non-technical stakeholders?
- How do you balance client-specific requirements with data governance best practices?
- How do you ensure data governance solutions are user-friendly and adopted by business users?
- How do you ensure data governance strategies are scalable and adaptable to future technologies such as AI?
- What trends do you see shaping the future of data governance?
- How do you stay updated on emerging data governance tools, standards, and practices?
- What data governance or data management certifications do you hold (e.g., CDMP, DCAM, CDMC)?
- How do certifications and frameworks influence your approach to data governance?