What is a Data Engineer?

A Data Engineer at Munich Re builds the data foundations that power underwriting, pricing, portfolio steering, claims analytics, risk modeling, and regulatory reporting across a global reinsurance business. You will design reliable pipelines, architect scalable data platforms, and ensure that the right data is in the right shape at the right time—securely and compliantly. Your work directly enables underwriters, actuaries, and data scientists to make faster, better decisions on complex, high-stakes risks.

This role is uniquely impactful because Munich Re’s products rely on deep, high-quality data—from catastrophe model outputs and market submissions to claims feeds, IoT/telematics, and climate datasets. Expect to contribute to initiatives like IFRS 17 data preparation, catastrophe exposure ingestion and curation (e.g., RMS/AIR outputs), pricing and portfolio dashboards, and near-real-time data services for underwriting. If you enjoy turning messy, distributed, and sensitive data into robust, governed data products, this is an intellectually rich and business-critical seat.

You will partner with cross-functional teams across Princeton, New York, and global hubs, building on modern stacks (e.g., Python, SQL, Spark, Snowflake/Databricks, Airflow/dbt, Kafka, and AWS/Azure) while embedding data quality, lineage, and compliance from day one. This is a role for builders who take ownership, communicate clearly, and deliver trustworthy data systems in a regulated environment.

Getting Ready for Your Interviews

Your interview preparation should focus on strong SQL and data modeling, modern data platform architecture, distributed processing, and a clear understanding of governance and compliance in a financial services context. Expect scenario-based discussions that connect engineering choices to underwriting, pricing, risk, and reporting outcomes. Be ready to communicate trade-offs crisply and show how you drive reliability, cost-efficiency, and data quality.

• Role-related Knowledge (Technical/Domain Skills) - Interviewers assess depth in SQL, Python, ETL/ELT, orchestration, data modeling, and distributed systems (e.g., Spark, Kafka). Show comfort with modern warehouses/lakehouses (Snowflake, Databricks), plus CI/CD, testing, and observability. Domain familiarity—insurance/reinsurance data, IFRS 17, catastrophe modeling inputs—will set you apart.
• Problem-Solving Ability (How you approach challenges) - You’ll be evaluated on how you decompose ambiguous data problems, reason about constraints (latency, cost, lineage), and build maintainable solutions. Interviewers look for structured thinking, clear assumptions, and measurable outcomes.
• Leadership (How you influence and mobilize others) - Munich Re values engineers who can align stakeholders, set engineering standards, and mentor others. Demonstrate how you drive data quality, advocate for platform improvements, and lead cross-team decisions without formal authority.
• Culture Fit (How you work with teams and navigate ambiguity) - Expect questions about collaborating with underwriters, actuaries, and data scientists, managing competing priorities, and learning from failure. Show high judgment, humility, and a bias for clarity, documentation, and follow-through.
• Risk & Governance Mindset - Data at Munich Re is sensitive and regulated. Interviewers look for engineers who proactively design for privacy, access control, lineage, and auditability—balancing speed with control.

Interview Process Overview

For the Data Engineer role, Munich Re’s process is designed to be structured, conversational, and insight-oriented. You’ll experience discussions that connect your technical depth to business impact—how you shape data into reliable products under real-world constraints. The tone is professional and collaborative, with space for you to ask detailed questions about tech stack, data domains, and team ways of working.

Expect a moderate level of rigor paced to respect your time, typically converging within a few weeks. You may encounter a technical deep-dive, an applied data case or code exercise, a system design conversation, and a behavioral session focused on collaboration and ownership. Feedback is generally prompt and constructive; candidates frequently note smooth coordination and helpful guidance throughout.

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."

This word cloud highlights the most frequent interview focus areas—expect heavier emphasis where terms are largest (e.g., SQL, Spark, Snowflake/Databricks, Airflow/dbt, Kafka, governance). Use it to prioritize your study plan: master the core themes first, then reinforce with scenario practice in adjacent topics.

Key Responsibilities

In this role, you will architect, build, and operate production-grade data products that support underwriting, pricing, claims analytics, catastrophe risk, and financial/regulatory reporting. You will partner with underwriters, actuaries, data scientists, and finance to translate business needs into robust pipelines and well-modeled datasets.

• Design and deliver end-to-end pipelines (batch and streaming) with strong SLAs, data contracts, and lineage.
• Model curated data layers (bronze/silver/gold) to power analytics, dashboards, and APIs for decision-making.
• Operationalize quality controls (testing, validation, anomaly detection) and implement observability across jobs.
• Embed governance and security (RBAC, encryption, masking, approvals) consistent with global policies.
• Collaborate cross-functionally to align on definitions, resolve data issues, and continuously improve data products.
• Drive platform excellence via CI/CD, infrastructure-as-code, cost management, and documentation.

Expect to iterate on strategic projects such as IFRS 17 data preparation, exposure ingestion and roll-ups, portfolio steering dashboards, and data services for underwriting submissions. You will own reliability and continuously improve performance, developer ergonomics, and user experience.

Role Requirements & Qualifications

Strong candidates combine deep technical skill with a risk-aware, product-oriented mindset. You’re comfortable shipping production systems, aligning stakeholders, and navigating complex data landscapes.

• Must-have technical skills

  • SQL and Python for robust transformations, testing, and tooling
  • ETL/ELT and orchestration (e.g., Airflow, dbt) with idempotent, observable pipelines
  • Distributed processing (e.g., Spark) and streaming (e.g., Kafka) fundamentals
  • Cloud data platforms (e.g., Snowflake or Databricks) and at least one major cloud (AWS/Azure)
  • Data modeling and contracts, including SCDs, CDC, and versioned tables
  • CI/CD, IaC, and security basics (Git, Terraform, secrets management, RBAC, encryption)
  • Data quality and lineage (dbt tests, Great Expectations, catalogs)

• Nice-to-have skills

  • Insurance/reinsurance domain exposure (underwriting, claims, catastrophe modeling outputs)
  • IFRS 17 or solvency reporting data knowledge
  • Geospatial/time-series experience (PostGIS, Delta Lake optimizations)
  • Cost governance, advanced observability, or differential privacy techniques

• Experience level

  • Experience may range from intern/junior (projects, internships in Princeton, NJ) to experienced hires. What matters is demonstrated ownership, clear impact, and strong fundamentals.
  • Highlight end-to-end delivery, measurable reliability/quality improvements, and collaboration with non-technical stakeholders.

• Soft skills that stand out

  • Structured communication, stakeholder alignment, and crisp documentation
  • Ownership and resilience in ambiguous, compliance-sensitive contexts
  • Pragmatism in balancing speed, cost, and control

Common Interview Questions

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.

Use this interactive module on Dataford to practice questions by category, capture your answers, and benchmark against model responses. Focus your repetitions on weak areas surfaced by practice results and simulate time-boxed, out-loud answers.

Frequently Asked Questions

Q: How difficult is the interview, and how much time should I allocate to prepare?
Expect a moderate level of rigor focused on depth over trick questions. Allocate 2–4 weeks for targeted prep: SQL and modeling drills, one to two system design run-throughs, and a governance/security refresher tailored to financial data.

Q: What makes successful candidates stand out?
Clear, structured thinking; demonstrable ownership of production pipelines; and a proactive governance mindset. Candidates who tie engineering choices to underwriting, pricing, and reporting outcomes tend to excel.

Q: What is the culture like for data teams at Munich Re?
Professional, collaborative, and product-oriented with an emphasis on reliability and compliance. Cross-functional partnership with underwriters, actuaries, and data scientists is routine and valued.

Q: What is the typical timeline and feedback cadence?
Processes are generally well-coordinated with prompt, constructive feedback. Keep momentum by confirming availability early and responding quickly to scheduling and materials requests.

Q: Is the role remote, hybrid, or on-site?
Expect hybrid collaboration aligned to team location (e.g., Princeton, NJ or New York, NY) with flexibility based on business needs. You’ll also work with global colleagues across time zones.

This module provides current compensation insights for the Data Engineer role, including base salary ranges and variable components where available. Use it to calibrate expectations; keep in mind that final compensation reflects location, experience, and scope, with competitive benefits typical of a global financial services leader.

Other General Tips

• Clarify ambiguity early: Restate the problem, confirm constraints (SLA, data volumes, latency, cost), and align on success metrics before diving in.
• Show your SLAs and SLOs: When describing past systems, cite concrete uptime, latency, cost-per-run, and data downtime reductions to demonstrate operational maturity.
• Bring artifacts: Reference sanitized diagrams, dbt docs, or runbooks to illustrate your standards for contracts, lineage, and observability.
• Narrate trade-offs: Explicitly weigh options (batch vs. stream, Snowflake vs. Databricks features, storage formats) and justify with business impact.
• Prepare domain fluency: Review basics of underwriting, claims, catastrophe exposure, and IFRS 17 so your designs map directly to stakeholder needs.

Summary & Next Steps

A Data Engineer at Munich Re builds the trusted data systems that power underwriting, pricing, risk analytics, and regulatory reporting worldwide. The role is technically demanding and business-critical—ideal for engineers who enjoy turning complexity into reliable, governed data products with visible impact.

Center your preparation on SQL/Python excellence, data modeling, distributed pipelines and orchestration, and governance/security in regulated environments. Practice structured, scenario-based answers that connect engineering trade-offs to outcomes for underwriters, actuaries, and finance. Use the Dataford modules in this guide to focus your study plan and rehearse effectively.

Approach your interviews with clarity and confidence. Demonstrate ownership, communicate trade-offs, and show how you build durable, observable systems that scale. You’re ready to deliver the data foundations Munich Re relies on—lean into your strengths and make your impact visible.