Databricks Software Engineer Behavioral Interview — Lakehouse culture and team fit

This behavioral interview assesses how a software engineer operates in Databricks’ high-impact, customer-obsessed, data-and-ML-centric environment. Interviewers focus on ownership in ambiguous settings, collaboration with cross-functional partners (PM, design, solutions architects, support), customer empathy for data and AI workloads, and pragmatic execution at scale. What interviewers evaluate: - Ownership and bias for action: taking end-to-end responsibility for services, pipelines, or platform components, especially when requirements are evolving. - Customer impact and product thinking: understanding user pain (data engineers, ML practitioners, platform admins) and tying decisions to measurable outcomes (latency, reliability, cost per TB processed, model performance, adoption). - Collaboration across the Lakehouse stack: partnering effectively with infra, data, and ML teams; communicating trade-offs among performance, reliability, cost, and security. - Operating production systems: on-call maturity, SEV handling, postmortems, and raising quality bars (testing, observability, change management). - Security, privacy, and governance mindset: handling data responsibly, least-privilege access, incident containment, and alignment with enterprise requirements. - Learning and humility: curiosity, feedback-seeking, and contributing to or leveraging open source (Spark, Delta Lake, MLflow) constructively. Structure and timing (typical 60 minutes): - 5 min: rapport, role context, and what great outcomes look like on the team. - 10 min: candidate career arc with impact highlights (scope, metrics, stakeholders). - 25 min: deep dives (2–3 scenarios) using STAR; interviewer probes for decisions, risks, and data used. - 10 min: follow-ups on pitfalls, alternative paths, and lessons learned. - 10 min: candidate questions about team charter, roadmap, and how success is measured. Sample Databricks-style prompts: - Tell me about a time you owned a service or data/ML pipeline end to end and had to ship under ambiguity. How did you derisk and measure impact? - Describe a customer escalation (e.g., reliability, performance, or cost spike). How did you triage, communicate, and prevent recurrence? - Walk me through a disagreement about system design or roadmap priority. How did you influence without authority and align on trade-offs? - Share a time you improved developer productivity or reliability (tests, CI/CD, observability, infra as code). What were the before/after metrics? - Talk about working with or contributing to open source (Spark/Delta/MLflow or similar). What constraints did the community or API stability introduce? - Give an example where security, privacy, or data governance shaped your approach. What guardrails or reviews did you implement? What strong signals look like: - Clear problem framing with metrics (e.g., p95 latency, weekly failure rate, compute spend, model F1) and explicit trade-offs. - Evidence of customer empathy, proactive comms, and crisp incident handling (runbooks, blameless postmortems, action items closed). - Collaboration that moves the needle across teams; thoughtful disagreement followed by alignment and execution. - Continuous improvement mindset: reducing toil, simplifying architecture, or paying down the right debt. Common red flags: - Vague impact or lack of metrics; optimizing locally without customer benefit. - Hand-wavy incident narratives, blame-shifting, or weak prevention. - Over-indexing on perfect code over shipping valuable, safe increments. - Ignoring data security or governance considerations in design and operations. Logistics notes candidates often observe: - Expect structured, probe-heavy follow-ups; be ready to dive into design choices, metrics, and lessons. - Scheduling and communications typically come via official Databricks channels; bring concise STAR stories with measurable outcomes tailored to data/ML platform work.