Expect a blend of technical deep-dives, applied scenarios, and behavioral questions. Use the categories below to structure your preparation and practice answering out loud with clear assumptions and trade-offs.

Technical / Domain Questions

These probe fundamentals and how you apply them in insurance/reinsurance contexts.

• How would you design a data model to support IFRS 17 cashflows and cohorts, ensuring auditability and lineage?
• What’s your approach to handling late-arriving facts and slowly changing dimensions in claims data?
• Explain how you’d validate completeness and consistency of catastrophe exposure data from multiple brokers.
• Describe partitioning and clustering strategies in Snowflake/Databricks for large policy/claims tables.
• How do you enforce and monitor data contracts across upstream APIs and downstream dashboards?

System Design / Architecture

You’ll outline end-to-end systems with reliability, cost, and governance in mind.

• Design a pipeline to ingest RMS/AIR outputs, standardize schemas, and publish curated exposure metrics daily.
• Propose a streaming architecture for underwriting submissions with schema evolution and DLQs.
• Build an observability plan for critical pipelines: metrics, SLOs, alerting, and incident response.
• Walk through a multi-environment promotion strategy (dev/test/prod) with CI/CD and approval gates.
• How would you secure sensitive data while enabling data science experimentation?

SQL & Coding

You’ll demonstrate correctness, performance, and readability under realistic constraints.

• Write SQL to compute loss ratio and combined ratio by product/quarter, handling late-arriving claims.
• Refactor a slow window-function query and explain your optimization choices.
• Implement an idempotent PySpark job for incremental loads with watermarking.
• Show how you’d test a dbt model and wire it into CI with data quality gates.
• Parse semi-structured JSON submissions into normalized tables and document assumptions.

Behavioral / Leadership

Munich Re values clarity, ownership, and collaboration across functions.

• Tell us about a time you improved data quality or lineage at scale—what changed and how did you measure it?
• Describe a situation where you mediated conflicting stakeholder needs (speed vs. control). What trade-offs did you make?
• When have you navigated ambiguous requirements? How did you converge on a solution?
• Give an example of mentoring or raising engineering standards on a data team.
• Describe a production incident you owned—root cause, remediation, and prevention.

Problem-Solving / Case Studies

Expect practical, scenario-based prompts where you propose and defend solutions.

• Given incomplete exposure data before a reporting deadline, how would you triage, backfill, and communicate risk?
• A pipeline’s cost doubled month-over-month—diagnose, propose fixes, and set guardrails.
• A downstream dashboard shows inconsistent portfolio metrics—trace and resolve data lineage issues.
• Design a reconciliation process for streaming vs. batch ingestion of claims updates.
• Propose a minimal viable data product to support a new underwriting use case in four weeks.

This timeline illustrates the common stages, from initial conversations through technical assessments to panel or hiring manager discussions. Use it to plan your preparation cadence: deepen your fundamentals early, reserve time for a take‑home or live exercise, and prepare thoughtful questions for cross-functional conversations. Maintain momentum by confirming availability windows and promptly sharing any requested materials.

Deep Dive into Evaluation Areas

SQL and Coding

Expect SQL to be central: Munich Re depends on well-modeled, queryable datasets with predictable performance. You’ll be assessed on writing robust SQL, optimizing queries, and translating complex transformations into maintainable code. Python proficiency for data manipulation, validation, and tooling is also common.

Be ready to go over:

• SQL fluency: Joins, window functions, CTEs, incremental loads, late-arriving data
• Performance tuning: Partitioning, clustering, statistics, query plans, cost trade-offs
• Python data tasks: Pandas/PySpark transformations, data validation, CLI tools
• Advanced concepts (less common): SQL anti-patterns, query rewrites, adaptive execution, vectorized UDFs

Example questions or scenarios:

• "Given denormalized policy and claims tables, write SQL to compute loss ratios by product and quarter, handling late-arriving claims."
• "Refactor this slow Snowflake query and explain the performance improvements."
• "Build a PySpark job to deduplicate and standardize broker submissions with fuzzy matching."

Data Modeling and Warehousing

Munich Re values consistent data contracts, dimensional models where appropriate, and governed semantic layers. You’ll discuss modeling choices that balance flexibility with auditability across IFRS 17, pricing, exposure, and claims domains.

Be ready to go over:

• Modeling patterns: Star/snowflake schemas, vaults, data contracts, slowly changing dimensions
• Data products: Gold/silver/bronze layers, dbt modeling, versioning strategies
• Quality and lineage: Tests (e.g., Great Expectations/dbt tests), cataloging, documentation
• Advanced concepts (less common): Surrogate key strategies, change data capture (CDC), time-travel/versioned tables

Example questions or scenarios:

• "Design a warehouse model to support IFRS 17 cashflows and cohorts with transparent lineage."
• "How would you structure a data product for cat exposure rollups with drill-down to location-level attributes?"
• "Show how you’d enforce and test a data contract across upstream APIs and downstream dashboards."

Distributed Data and Orchestration

You’ll be evaluated on building resilient, observable pipelines that scale. Expect to speak to orchestration choices, event-driven patterns, and reliable batch/stream processing.

Be ready to go over:

• Orchestration: Airflow, DAG design, retries, SLAs, backfills, idempotency
• Distributed processing: Spark tuning (partitions, shuffle), autoscaling, checkpoints
• Streaming: Kafka topics, schemas (Avro/Protobuf), exactly-once semantics, dead-letter queues
• Advanced concepts (less common): CDC into lakehouse, schema evolution strategies, replay/reconciliation

Example questions or scenarios:

• "Design a pipeline to ingest RMS/AIR outputs daily, validate exposure completeness, and publish curated tables."
• "Walk through handling a corrupted Kafka message in a streaming job with data quality guarantees."
• "How do you implement safe backfills for historical IFRS 17 transformations?"

Cloud, DevOps, and Observability

Reliability and cost transparency are core. You’ll discuss cloud services, IaC, CI/CD, secrets management, and end-to-end observability for data platforms.

Be ready to go over:

• Cloud platforms: AWS/Azure data services, Snowflake/Databricks deployment patterns
• IaC and CI/CD: Terraform, GitHub Actions/Azure DevOps, environment promotion
• Observability: Metrics, logs, lineage, data downtime detection, incident response
• Advanced concepts (less common): Cost governance (e.g., warehouse resource profiles), canary deployments, blue/green data releases

Example questions or scenarios:

• "Propose an observability plan for a critical underwriting pipeline, including SLOs and alerting."
• "Explain how you would secure secrets and rotate keys across environments."
• "Design CI/CD for dbt models with automated testing and approval gates."

Governance, Security, and Insurance Domain Context

As a regulated enterprise, Munich Re emphasizes privacy, access control, auditability, and domain correctness. You’ll be assessed on embedding governance into architecture and understanding key insurance/reinsurance data nuances.

Be ready to go over:

• Security & privacy: PII/PHI handling, encryption, row/column-level security, tokenization
• Compliance: GDPR, SOX-like controls, audit trails, approval workflows, data retention
• Domain: Policies, coverages, exposures, cat modeling outputs, claims lifecycles, IFRS 17/solvency reporting
• Advanced concepts (less common): Differential privacy trade-offs, purpose-based access, fine-grained lineage for regulatory attestations

Example questions or scenarios:

• "Design access controls for portfolio dashboards with restricted line-of-business and region visibility."
• "How would you track lineage and produce an audit report for IFRS 17 adjustments?"
• "Discuss trade-offs when anonymizing claims data for data science exploration."