claude-resume-kit/output/SBB_DataEngineer_AssetMgmt/interview_brief_sbb_2026-09-09.md

Interview Brief — SBB, Data Engineer Asset Management Infrastrukturanlagen

When: Mittwoch, 9. September 2026, 08:30 · 45 Minuten · Microsoft Teams Stage: 2 of 4. The posting's own process is: Bewerbung → Virtuelles Kennenlernen mit HR und Führungskraft → Persönliches Kennenlernen → Finaler Entscheid. Who is likely on the call: HR plus Andri Wienandts, People Leader (+41 79 364 62 53). This is not a pure HR screen — the hiring manager is in the room. Job ID: 103755 · Bern / Work Smart · 60–100% · Anforderungsniveau K, Regionalzulage Stufe 1 Submitted: 2026-08-25 · Core · Evidence Fit 79/100 · Document Quality 92/100 · cold channel

45 minutes shared between two people is short. Assume ~25 min of them asking, ~10 min of you asking, ~10 min logistics. You will not get a second cheap shot at the money and scope questions before the in-person round, so do not let them fall off the end.

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1. The one thing to understand before you dial in

A cold submit converted. You never called Andri Wienandts, despite his mobile being printed on the posting — the channel plan said to call first and it did not happen. It got an interview anyway.

More telling: your resume never contains the words Snowflake, dbt or Power BI, and all three are named in the required-qualifications block. An ATS keyword screen configured on those literals would have dropped you. It didn't. Someone read the dossier and made a judgement. That means the honest substitution story (Teradata/Oracle/AWS → Snowflake; Airflow/PySpark → dbt; Spotfire → Power BI) already survived its first contact with a human — you are not walking in needing to apologise for it. Defend it the same way, calmly, as a tooling difference rather than a gap.

This is also the first interview in the evidence-first cohort — 6 applications, 2 rejections, this is the first conversion.

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2. Your genuine edge — lead with this

For a role whose stated purpose includes vorausschauende Wartung on physical infrastructure assets, you have a combination most candidates on this req will not:

• Industrial sensor and asset data, in production. Bosch fab: defect-management records, wafer inspection images, PCM electrical parameters (BS-2).
• Anomaly detection on streaming data. The ELK/Kafka PoC with Grafana/Prometheus/Loki monitoring (BS-4).
• Containerised ML inference into a plant that cannot stop. Docker/Kubernetes/Ansible into 24/7 semiconductor production, after which defect-image classification ran fully automated (BS-1).
• A master's thesis on vibration-based condition monitoring of machine tools — rule-based reasoning plus a 7-10-3 ANN.
• The operational half of this job, today, at a peer Swiss enterprise in the same city. Component Owner with on-call, data quality, governance and SLA accountability (SW-2) is precisely "Betrieb" and "Verantwortung für die Qualität und Nachhaltigkeit technischer Lösungen".

Thesis caveat — hold this line: it is a methods prototype, never a validated system. No real operational data (training data came from a cited thesis; test data was modified plus a random generator), no accuracy figures exist, and PSO was surveyed only, never implemented. The throughput figure (~500 samples/s against 72.9 kSPS sensors) is barred from external use. Describe it as genuine interest and method familiarity, not as a result.

Where they beat you: named-product fluency on the exact stack, and rail domain — the asset taxonomy, EN 50126 RAMS vocabulary, the swissTAMP/RIS ecosystem.

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3. The four questions you must not leave without asking

These were never resolved before submitting. They are now live screening topics, and this call is the natural place for them.

Q1 — Anforderungsniveau K, the actual band

"Die Stelle ist im Anforderungsniveau K mit Regionalzulage Stufe 1 ausgeschrieben. Könnt ihr mir die Bandbreite für dieses Niveau nennen, und wo eine Person mit meinem Profil typischerweise einsteigt?"

Why it is fair game: SBB publishes its pay system and campaigns publicly on Lohngleichheit and transparency. Asking about a collectively-bargained band is normal, not pushy. The figures are simply not public, so asking is the only way to get them.

Your bar: Swisscom ~140k; you move at 180k+ all-in. K is very likely below that. The Bern/Thun WLB-exception tier deliberately accepts below-bar comp for a local seat with a real mission — but it has never accepted a below-level move (see Q2). Know before the call which trade you are actually willing to make, so you can react in the moment rather than going quiet.

Q2 — Level and design ownership (the crux)

"Die Ausschreibung liest sich als Senior-IC-Rolle. Trägt sie Design-Verantwortung für die Pipelines und das semantische Modell, oder wird die Architektur an anderer Stelle gesetzt?"

Why this decides it. Every other application in your log was a stretch upward. This one reads below your current Staff (Engineer IV) + Component Owner scope: it asks you to mentor other data engineers but the only architecture language is "Datenarchitekturen entwerfen" as a personal skill, not an ownership mandate. This is the same shape as BKW, which you declined as a lateral in this same Bern tier. Ask it as diligence — it also surfaces whether the seat can grow.

Q3 — RAMSI, and how much of the job it really is

"Was ist RAMSI genau, und welchen Anteil der Rolle macht die Weiterentwicklung der Webanwendung aus?"

One of six responsibility lines is web development in Java Spring Boot / Angular. Your Java is professional but historical, Spring Boot was a PoC contribution roughly a decade ago, and Angular is absent entirely. Do not oversell it — ask what share it is. It may be work you do not want, and that is worth knowing now.

Do not assert what RAMSI is. It is not publicly documented; the RAMS/EN 50126 family is an inference. Ask.

Q4 — Kidz Care, if childcare matters to the decision

SBB covers "bis zu 90 %" of Betreuungskosten but scales it on gross household income, and the bracket table is intranet-only. At this band, realistically far below 90%. Worth one question if it affects the maths.

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4. Questions they will ask — and the honest answer

Their question	Your line
Snowflake? dbt?	"Not by name. I have run the same shape of work on Teradata and Oracle, then on AWS — Glue, Athena with Iceberg, Redshift — with Airflow for orchestration. The modelling, testing and lineage discipline transfers; the syntax is what I'd pick up."
Power BI?	"I co-owned a BI platform rather than just consuming one — TIBCO Spotfire at Bosch, including C# extensions and wafer-map visualisations, which I co-presented at the TIBCO Analytics Forum 2022."
Argo Workflows / Helm?	Airflow is a direct orchestrator substitution; Kubernetes is current and production. Do not claim Helm — it is not canonical.
Predictive maintenance experience?	Lead with Bosch sensor/process data and the ELK/Kafka anomaly-detection PoC. Then the thesis, framed as a methods prototype.
Why leave Swisscom?	Make it about the work, not the band. Physical infrastructure, asset condition data with a visible public consequence, a Bern seat, and a technical brief that sits on your actual thesis. Do not volunteer that you expect a pay cut.
Trade-offs between latency, consistency and cost?	A named required skill and a real strength — Component Owner for business-critical Oracle/Kafka → Teradata ETL with on-call and SLAs. Have one concrete example where you chose batch over streaming, or accepted staleness for cost.
Mentoring / fachliche Entwicklung?	Application Owner at Bosch: documentation, user training, vendor coordination; Swisscom Security Champion for the team. Real, but framed as team roles rather than line management.

Two things to keep off the table: don't claim Snowflake, dbt, Power BI, Helm, Argo, Spring Boot or Angular by name; and don't quote any unverified metric — nearly everything in your record is metrics: unverified, and inventing a number in an interview is the same error as putting one on the resume.

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5. Company context worth showing you know

• swissTAMP is SBB's central source for asset condition and behaviour data — inspection, measurement and operating data, trends and forecasts. SBB describes it explicitly as enabling the move "from reactive through proactive to predictive maintenance."
• Alongside it: RIS (Räumliche Informationssysteme, incl. Datenbank feste Anlagen) and Mess- und Diagnosetechnik (MUD), which runs automated condition monitoring of track, switches and power supply.
• SBB operates a Competence Center Predictive Maintenance.
• Funded and growing, not a backwater: there is an active EU-journal tender (235005-2026) for up to three new-generation infrastructure diagnostic vehicles. More diagnostic vehicles means more sensor volume — which is exactly what this pipeline seat exists to absorb. Good, specific thing to reference.
• Culture: #einfachDu — informal du from first contact, so match that register. Work Smart hybrid. Cover letter deliberately waived. 60–100% explicitly welcome.

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6. Practicalities

• 08:30 start — be on Teams early; test audio the night before.
• Match the du register from the first sentence.
• The posting is in German and the team is German-speaking; your resume was submitted in English with a Languages line naming German as mother tongue. Expect the call in German. Don't be thrown if they open in English.
• Bern-based, German native, EU citizen with a Swiss B permit, no relocation, no sponsorship — the cleanest practical fit in your whole log. Say so if the topic opens.
• Have ready: a concrete latency/consistency/cost trade-off story, and one example of mentoring or lifting another engineer.

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7. What to decide before 08:30

Not homework for them — homework for you:

1. If K comes in at, say, 130–150k all-in: is this still a yes? The WLB-exception tier says a Bern seat with a real mission can go below the 180k bar. It has never said below-level scope is acceptable.
2. If Q2 comes back "architecture is set elsewhere": is it still a yes? That is the BKW shape you already declined once.
3. If RAMSI turns out to be 40% Angular work: is it still a yes?

Any one of those can be a no, and it is much better to know your own answer before they say the number than to improvise it on camera at 08:40.

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PART II — Added 2026-09-08, the evening before

Sections 1–7 above are the strategy: what to ask, what to decide, what not to claim. Sections 8–11 below are the rehearsal: what the job is, how your record maps to it, and what to say when they ask. Same rules apply throughout — nothing here licenses a claim §4 forbids.

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8. The job, in one page

Team: Asset Management Infrastrukturanlagen — the people responsible for SBB's physical infrastructure assets (track, switches, power supply, structures). You would be the data engineer serving that asset-management function, not a generic analytics seat.

What the role actually does, from the six responsibility lines:

1. Build, operate and evolve data pipelines on SBB's Enterprise Analytics Plattform, which the posting says explicitly unites batch and streaming.
2. Integrate heterogeneous sources via APIs and Kafka — and the stated purpose of that integration is "Analysen, Dashboards sowie vorausschauende Wartung". Predictive maintenance is named in the job description, not inferred.
3. Build data products and semantic models on Snowflake and Power BI.
4. Evolve RAMSI — a web application (Java Spring Boot / Angular), plus its reports and dashboards, for the Fachspezialist:innen.
5. Work directly with the business to gather requirements and produce solutions that survive in operation ("nachhaltige Lösungen im Betrieb").
6. Share knowledge and develop other data engineers — explicitly "die fachliche Entwicklung anderer Dateningenieur:innen zu fördern".

What they say they want: multi-year hands-on experience designing, building and operating cloud data platforms on a modern stack (ETL/ELT, DWH, streaming, orchestration — z. B. Snowflake, dbt, Helm, Argo Workflows, Kafka); the ability to design scalable, cost-efficient architectures and judge trade-offs between latency, consistency and cost; data modelling (star/snowflake), Power BI, advanced SQL and query-performance tuning; and Python software engineering including tests, CI/CD, packaging and deployment.

Read the shape, not the tool list. Four of the six responsibilities and three of the four technical requirements are about building and running pipelines and data products in production, with the business, under operational accountability. That is your job today, at a peer Swiss enterprise, in the same city. The named products are the surface. The z. B. in front of the tool list is doing real work — they wrote "for example", not "must have".

Two honest asymmetries to hold in mind:

• RAMSI (line 4) is the one responsibility on a stack you do not have. It is one of six. Ask what share it is (§3 Q3) — do not assume it is small, and do not assume it is large.
• You still do not know what RAMSI is. It is not publicly documented. The RAMS / EN 50126 reading is an inference and you must not present it as knowledge. Ask.

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9. How your record maps — the honest ledger

Use this to know which ground is solid, not to recite. Bold = say this out loud if the topic opens.

What they asked for	Your evidence	Call it
Build/operate/evolve pipelines on a cloud analytics platform, batch and streaming	Component Owner for business-critical Fulfillment ETL across Oracle, Kafka, Python, Teradata — including on-call. Migrated Fulfillment and Product Analysis pipelines onto AWS with Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation.	Direct — your strongest ground
Integrate sources via APIs / Kafka	Kafka in production at Swisscom today; ELK/Kafka anomaly-detection PoC at Bosch.	Direct
Operating, not just building ("nachhaltige Lösungen im Betrieb", quality, reliability)	Component Ownership: data quality, governance, incident handling, on-call, 2nd/3rd-level RCA.	Direct — and it is the half most candidates are weak on. Lead with it.
Data products + semantic models	Build and model governed data products in Swisscom's company-wide Data Mesh; onboard source systems; maintain active metadata and lineage (Atlassian Compass).	Direct in substance
…on Snowflake and Power BI	Not by name. Teradata, Oracle, Redshift, Athena/Iceberg; Spotfire co-owned with C# extensions.	Gap on the names — see §10 Q4
Trade-offs: latency / consistency / cost	Real, but you must bring one concrete example. See §10 Q8 — do not improvise this one.	Direct if prepared
Advanced SQL + query-performance tuning	Named explicitly in your Bosch bullet: tuned query performance over Oracle and Hadoop/Impala for analysis teams.	Direct
Data modelling (star/snowflake schemas)	Data modelling is canonical and on the resume. Dimensional modelling by that vocabulary is thinner — speak to modelling for consumption and reuse, and don't over-claim Kimball fluency you can't back.	Direct, stated plainly
Python SE: tests, CI/CD, packaging, deployment	Python apps on Kubernetes with GitLab CI/CD, automated tests, packaging, deployment through to production. Plus Vizrt test automation as CI quality gates, Generali BDD suite, Fraunhofer Jenkins.	Direct — deep, and it spans 10 years
Orchestration (dbt / Argo / Helm named)	Airflow in production. Kubernetes current. Do not claim Helm or Argo.	Adjacent — substitute honestly
Develop other data engineers, share knowledge	Application Owner at Bosch (user training, documentation, vendor coordination); trained colleagues in Generali's Java community; Security Champion; Leadership Cohort 2025. Team roles, not line management — say it that way.	Adjacent, and genuinely evidenced
Vorausschauende Wartung	Bosch fab sensor/process data — defect records, wafer inspection images, PCM electrical parameters. ML inference containerised into a 24/7 fab. ELK/Kafka anomaly detection. M.Eng. thesis on vibration-based condition monitoring of CNC machine tools.	Your differentiator. Nobody else on this req likely has all four.
RAMSI — Java Spring Boot / Angular	Java professional but historical; Spring Boot a PoC contribution ~a decade ago; Angular absent.	Gap — see §10 Q7

One-line version, if you only remember one thing: you already do the operating half of this job, in production, under on-call, in Bern — and you bring industrial condition-monitoring experience the generic data-engineering candidate does not.

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10. The answer bank — ordered by likelihood × damage

German is scripted only where the wording itself carries risk. Everywhere else you have the content and the order; you are a native speaker, so say it in your own words.

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Q1 · "Was sind deine Lohnvorstellungen?" — asked of you, probably early

⚠ The single highest-risk moment in the call.

The trap: K is a capped, collectively-bargained band. There is no lever at the top of it. Name 180k and you look mispriced and the conversation cools. Name a low figure and you have anchored yourself under a ceiling that was already fixed. Both directions lose. So don't name a number — flip it into the band question you were going to ask anyway.

"Ihr habt die Stelle ja transparent im Anforderungsniveau K mit Regionalzulage Stufe 1 ausgeschrieben — insofern bewerbe ich mich bewusst in ein publiziertes Band hinein. Könnt ihr mir die Bandbreite nennen und sagen, wo jemand mit meinem Profil typischerweise einsteigt? Dann kann ich das konkret einordnen."

This is not evasive — it is the correct answer to a published-band role, and it lands your §3 Q1 for free. If they push for a figure anyway: "Ich komme aus einer Staff-Rolle in der Privatwirtschaft, das Niveau kennt ihr. Mir ist wichtiger zu verstehen, wo K liegt, bevor ich eine Zahl in den Raum stelle."

Then stop talking. Let them answer. Write the number down (§ worksheet).

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Q2 · "Du bist aktuell Staff Engineer und Component Owner — warum diese Stelle?"

The overqualification question. Near-certain, and HR hears "flight risk".

They are asking: will you be bored in a year and leave? Answer the risk, not the flattery.

Ground it in the work, in this order:

1. The domain is the pull. Physical infrastructure assets with condition data and a visible public consequence — that is the subject of your master's thesis and of your Bosch years, and it is not what you work on today. Today the data is telco fulfilment.
2. The operating half is what you're good at and want to keep. Not a step away from ownership — the posting explicitly wants someone to carry quality and sustainability in Betrieb.
3. Bern. You live in Thun. This is the cleanest practical fit in your whole search.
4. Then turn it into your Q2 (§3): "Deswegen interessiert mich auch, wie viel Design-Verantwortung die Rolle trägt — trägt sie die Architektur für die Pipelines und das semantische Modell, oder wird die an anderer Stelle gesetzt?"

That last move is the good one: it converts a defensive question into your most important diligence question, and it signals you are thinking about scope rather than title.

Do not say: that you expect a pay cut, that you are frustrated at Swisscom, or anything that reads as "I'll take anything in Bern."

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Q3 · The 19-month hole on the CV: Oct 2013 → May 2015

⚠ A Swiss HR first round walks the CV chronologically. This will be asked, and nothing on the page you submitted explains it. Education ends Oct 2013; Generali starts May 2015. There is no Bundeswehr entry on the resume at all.

The honest fill is a strength, and it is two parts:

1. Officer service ran to November 2014. You were an officer in the Bundeswehr from 2008 until Nov 2014 — Second Lieutenant at separation — and the M.Eng. at Universität der Bundeswehr München was taken during that service. That explains both the gap and the degree institution in one sentence.
2. Then a first civilian role from Nov 2014 to May 2015, before Generali.

"Nach dem Master war ich noch bis November 2014 als Offizier bei der Bundeswehr — das Studium an der Universität der Bundeswehr lief ja innerhalb der Dienstzeit. Danach bin ich in die Privatwirtschaft gewechselt."

Important boundary: the standing rule that keeps Capgemini off your documents is a document rule. It does not bind a live conversation. If they ask directly what the first civilian role was, name it plainly — a six-month first role reads as completely normal; visibly declining to name a former employer reads badly. Do not volunteer it; do not conceal it.

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Q4 · "Snowflake? dbt? Power BI?" — the named-tool gap

Your resume contains none of these three words and they read it anyway and invited you. Someone made a judgement. Answer from that position — a tooling difference, not a confession.

"Nicht unter dem Namen. Ich habe dieselbe Art von Arbeit auf Teradata und Oracle gemacht und dann auf AWS — Glue, Athena mit Iceberg, Redshift — mit Airflow für die Orchestrierung. Die Modellierungs-, Test- und Lineage-Disziplin ist dieselbe; die Syntax ist das, was ich mir aneigne."

• Power BI: "Ich habe eine BI-Plattform mitverantwortet, nicht nur konsumiert" — TIBCO Spotfire at Bosch, C# extensions and wafer-map visualisations, co-presented at the TIBCO Analytics Forum 2022.
• Argo / Helm: Airflow is the honest orchestrator substitute; Kubernetes is current and in production. Do not claim Helm or Argo.

Then stop. Do not over-explain a gap they have already decided to look past.

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Q5 · "Erzähl uns etwas über dich" — the opener

90 seconds, not five minutes. Content and order (your own words):

1. Now: Staff Data, Analytics & AI Engineer at Swisscom in Bern; Component Owner for business-critical fulfilment pipelines — build and run, including on-call.
2. The move that shows range: migrated his domains' pipelines onto AWS; today builds governed data products in the company-wide Data Mesh. (Scope discipline: "my domains", "within Swisscom's Data Mesh" — never "I built the Data Mesh".)
3. Before that: Bosch semiconductor Dresden — industrial sensor and process data, ML inference into a fab that cannot stop, anomaly detection on streamed logs.
4. The hook to this role: "Und der Grund, warum genau diese Stelle: mein Master war schwingungsbasierte Zustandsüberwachung von Werkzeugmaschinen. Zustandsdaten von physischen Anlagen sind der Bereich, aus dem ich komme und in den ich zurück will."
5. Practical: German native, lives in Thun, EU citizen with B permit, no relocation needed.

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Q6 · "Warum SBB? / Warum weg von Swisscom?"

Make it about the work, never the band.

Toward SBB: physical infrastructure, asset condition data, a visible public consequence, a Bern seat, and a technical brief that sits on your actual thesis domain. If you want a specific, credible detail: SBB is procuring up to three new-generation infrastructure diagnostic vehicles (EU tender 235005-2026) — more diagnostic vehicles means more sensor volume, which is exactly what this pipeline seat absorbs. That shows you looked.

Away from Swisscom: keep it short, forward-looking, and non-negative. Telco fulfilment data is not the domain you want to spend the next decade in. Nothing about pay, nothing about people.

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Q7 · "Wie sieht es mit Java, Spring Boot und Angular aus?" (RAMSI)

Do not oversell, do not apologise. Then turn it into your §3 Q3.

"Java ist bei mir professionell, aber historisch — bei Bosch Datenservices in Java, bei Generali die Java-Community. Spring Boot war ein PoC-Beitrag vor rund zehn Jahren. Angular habe ich nicht. Was ich gern verstehen würde: Was ist RAMSI genau, und welchen Anteil der Rolle macht die Weiterentwicklung der Webanwendung aus?"

Do not assert what RAMSI is. You do not know. Asking is the correct move and also gets you the answer you need for decision 3 in §7.

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Q8 · "Wie beurteilst du Trade-offs zwischen Latenz, Konsistenz und Kosten?"

A named requirement and a genuine strength — but you must arrive with one concrete story. Do not improvise this. Pick one before 08:30, from Fulfillment ETL or the AWS migration:

• Where you chose batch over streaming because the consumer's decision cycle didn't justify the operational cost, or
• Where you accepted staleness to cut cost, or
• Where on-call reality drove a design choice — the thing you own at 03:00 shapes what you build.

Structure: situation → the options → what you chose and the reason → what it cost you. Naming the downside is what makes it credible.

No invented numbers. Nearly everything in your record is metrics: unverified. Describe the decision, not a benchmark.

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Q9 · "Erfahrung mit Predictive Maintenance?"

Your differentiator — lead with production, close with the thesis.

1. Bosch: fab sensor and process data — defect-management records, wafer inspection images, PCM electrical parameters.
2. ELK/Kafka anomaly-detection PoC with Grafana/Prometheus/Loki monitoring on continuously operating systems.
3. Containerised ML inference into 24/7 production, replacing manual image-based defect classification. (Integration and operation — not model development. Never claim you trained the model.)
4. Thesis: vibration-based condition monitoring of CNC machine tools; rule-based reasoning plus a 7-10-3 feed-forward ANN.

⚠ Thesis caveat, hold this line: it is a methods prototype, never a validated system. No real operational data. No accuracy figures exist. PSO was surveyed only, never implemented. The throughput figure is barred. Frame it as genuine method familiarity and motivation — not as a result. If asked how well it worked: "Das war ein Methodenprototyp, kein validiertes System — belastbare Genauigkeitszahlen gibt es dafür nicht." That answer makes you look more rigorous, not less.

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Q10 · "Wie förderst du andere Dateningenieur:innen?"

Real, but frame as team roles, not line management: Application Owner at Bosch (user training, documentation, vendor coordination); trained colleagues in Generali's Java community; Security Champion for the team at Swisscom (2025/2026); elected to the Swisscom Leadership Cohort 2025. Have one concrete example of lifting a specific colleague — that beats the list.

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Q11 · The routine HR set — decide these tonight, don't improvise

Three facts only you can supply. Write them down before the call:

• Kündigungsfrist at Swisscom: ______ (do not guess on the call)
• Earliest realistic start: ______ (posting says "ab sofort oder nach Vereinbarung")
• Pensum you want: ______ — the posting says 60–100% and HR will ask. A 90% option changes the CHF/hour maths materially (see worksheet). Have a position.

Also likely, and low-risk if you don't ramble:

• Stärken: ownership of things in production; translating between business and engineering; calm in incidents. Give an example, not adjectives.
• Schwächen: pick a real one with a correction in motion. Not "I work too hard." A defensible honest one here: named-product breadth — you go deep on a stack and pick up new tooling on the job rather than collecting tools. That is true and it pre-empts Q4.
• Arbeitsweise / agil: agile DevOps team, GitLab CI/CD, on-call rotation, requirements straight from business stakeholders.
• Konflikt / Fehler: have one story where something you owned broke and you carried the RCA. On-call gives you real material — use it.
• "Hast du Fragen an uns?" → your §3 Q1–Q4. Do not let this fall off the end at minute 44.

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11. The 45 minutes, budgeted

Two people, 45 minutes. Roughly: ~5 min intros → ~25 min them asking → ~10 min you asking → ~5 min logistics. Your four questions in §3 need that 10-minute window; if the conversation is running long, ask Q1 (the band) and Q2 (scope) first — those two decide whether this is real, and they are the ones you will not get a cheap second shot at before the in-person round.

Final reminders: match the du register from the first sentence (#einfachDu). Expect German; don't be thrown if they open in English — your resume was submitted in English. Be on Teams early and test audio tonight. And keep §4's two prohibitions in your head the whole time: no tool claimed that isn't yours, no number that isn't verified.