# Session: Google — Senior Data Engineer (Merchant Data Science)

## JD Info
- **File:** JDs/google_senior_data_engineer.txt
- **JD source:** live scrape 2026-06-15 via Playwright (Google careers board), re-verified live same day
- **URL:** https://www.google.com/about/careers/applications/jobs/results/87066954308690630-senior-data-engineer?location=Switzerland
- **Role:** Senior Data Engineer — Merchant Data Science team, Merchant Shopping Organization
- **Company:** Google (Alphabet)
- **Bundle:** Staff / Senior Data Engineer (primary, Tier 1) + Analytics Engineer (secondary bridge — BI/self-serve/stakeholder)
- **Format:** Resume (2-page, resume.cls) + 1-page cover letter. NON-USA target (Zürich) → omit US immigration line.
- **Level/Comp:** Level chip "Mid" despite "Senior" title — clarify L4 vs L5 at recruiter stage. US band shown $156–227k + 15% + equity. Zürich (Levels.fyi): L4 median ~CHF 240k total, L5 ~CHF 293k. **Even L4 clears the 180k+ all-in bar.**

## JD Analysis
### Requirements
| # | Requirement | Match | Evidence |
|---|-------------|-------|----------|
| 1 | Bachelor's / equivalent practical experience | Direct | M.Eng. Computer Aided Engineering (thesis grade 1.0) |
| 2 | 5+ yrs designing data pipelines + dimensional data modeling, sync/async integration, internal (Flume) + external (DataFlow, Spark) stacks | Direct (tool-bridge on Flume/DataFlow) | SW-1 (AWS Glue/Airflow/Redshift ETL), SW-6 (PySpark/Spark), SW-4 (Oracle/Kafka→Teradata), BS-4. Spark direct; Flume/DataFlow NOT used — bridge as transferable distributed processing, never claim |
| 3 | 5+ yrs coding in 1+ languages | Direct | Python (expert, 10+ yrs), Java, SQL, C# |
| 4 | 5+ yrs data infrastructure + data models, exploratory queries/scripts | Direct | SW data platform (S3/Glue/Athena/Redshift), Data Lake, SQL, data products/modeling |
| 5 (pref) | 5+ yrs statistical methodology + BI/data-consumption tools (Tableau, Power BI, DataStudio, Jupyter, collabs) | Bridge (MED) | Dashboards for B2B teams (SW-4); Jupyter/Pandas likely; named BI tools (Tableau/PowerBI) thin — verify in experience files, don't oversell |
| 6 (pref) | 3+ yrs project plans + on-time/in-budget/in-scope delivery | Direct | Component Owner (SW-2), Application Owner (BS-3), delivery ownership |
| 7 (pref) | 3+ yrs stakeholder partnering/management | Direct | SW-4 (PO/stakeholder, B2B product teams), BS-3 (vendors, cross-team) |
| 8 (pref) | ML for production workflows | Direct | BS-1 production ML inference in 24/7 fab |
| RESP | Advanced data engineering, data modeling, architectural frameworks | Direct | Data Mesh data products, data modeling, AWS architecture (iSAQB) |
| RESP | Design/build/scale data products incl self-serve tools + automated pipelines | **Direct — near verbatim** | SW-7 self-service governed data products consumed by downstream teams |
| RESP | Data infra, product quality, automated validation, data quality, reliability monitoring | Direct | SW data governance/SLAs, BS-4 observability/anomaly detection/monitoring |
| RESP | High autonomy, own projects conception→landing→impact | Direct | Component Owner + Application Owner titles; FC-1 built CI/CD from zero |
| RESP | Engineering excellence, robust data, sharp communication | Direct | Training/docs (BS-3), cross-team adoption, RCA |

### ATS Keywords
- **Core DE:** data pipelines, dimensional data modeling, data products, self-serve tools, automated pipelines, data infrastructure, data models, ETL/ELT, data warehouse, distributed data processing
- **Tools:** Spark (DataFlow/Flume — bridge, never claim), Airflow, SQL, Python, BigQuery (bridge via Athena/Redshift/Iceberg — do NOT claim by name), Glue, Kafka
- **Quality/reliability:** data quality, automated validation frameworks, reliability monitoring, data governance, SLAs, observability
- **Collaboration:** stakeholder management, cross-functional, bridge SWE/DE/DS, data-driven decision-making, multidisciplinary
- **Preferred:** statistical methodology, BI tools (see BI-tool note below), ML for production workflows, project delivery

### BI / Data-Consumption Tools (user-confirmed 2026-06-15 — usable, honest leveling)
- **TIBCO Spotfire (Bosch) — CO-OWNED platform + extended in C# + TAF 2022 speaker (VERIFIED).** Co-owned the Spotfire analytics platform serving fab engineers (+ Defect Management System); built C# extensions. **Co-presented at TIBCO Analytics Forum 2022** ("Understanding Spatial Variability in Semiconductor Products with Spotfire Map Charts"). Verified via community.spotfire.com scrape 2026-06-15. See [[taf_2022_spotfire]] + experience_bosch.md BS-5. Use TAF talk as CL hook + interview bridge.
- **Tableau (Swisscom)** — legacy DWH reporting; list as named tool at exposure weight, do NOT claim proficiency.
- **AWS QuickSight (Swisscom)** — new reporting; BASIC knowledge only. Mention lightly / fold under AWS; do not oversell.
- **Jupyter / Pandas** — covers the "collabs/notebooks" part of the qual.
- **Soft:** high autonomy/ownership, engineering excellence, sharp communication

### Gap Assessment
- **Direct:** data pipeline design, dimensional modeling, Spark, SQL, Python, data infrastructure, self-serve data products (flagship), data quality/validation, reliability monitoring, stakeholder mgmt, project delivery, production ML, autonomy/ownership
- **Bridge:** Flume/DataFlow/BigQuery (Google-internal/GCP — bridge via Spark/Airflow/Glue/Athena/Redshift/Iceberg, transferable, NEVER claim by name); named BI tools Tableau/PowerBI/DataStudio (MED — has dashboards/Jupyter, verify before claiming); statistical methodology (MED — has ML/analytics, don't oversell)
- **Gap (do NOT claim):** GCP/BigQuery/Dataflow hands-on, Google-scale data, e-commerce/marketplace domain. Honest gaps. Google explicitly accepts "equivalent practical experience"; he meets every minimum qual.

## Company Context
- **Mission:** Make Google the best place for merchants to connect with shoppers; the **Shopping Graph** (60B+ products indexed) is central. Merchant Shopping Org builds the data foundation behind Shopping + AI experiences (Gemini, AIM).
- **This team:** **Merchant Data Science** — global team with members in US, London, **Zürich**. Builds scalable data products that empower data-driven decision-making; partners with PMs, engineers, data scientists. This role explicitly "bridges the gap between software engineering, data engineering, and data science."
- **Google data stack (context):** Flume/FlumeJava, Dataflow, BigQuery (Dremel/Capacitor/Colossus/Borg), ZetaSQL, Spanner. JD names Flume + DataFlow. Dennis's AWS/Spark/Airflow analog is conceptually transferable (distributed processing, columnar warehouses, SQL-on-data-lake).
- **Culture:** Data-product engineering excellence, autonomy, cross-functional, scale. Equivalent-practical-experience accepted.
- **"Why them" angle:** Dennis already builds self-service, governed data products consumed across teams at telco scale — the exact "durable data products that bridge SWE/DE/DS" this team builds. Add production-ML + data-quality/reliability discipline. Zürich team = local to Bern.

## Framing Strategy
- **Lead narrative:** "Data engineer who designs and owns self-service, governed data products and the pipelines/dimensional models behind them — built for downstream teams to make data-driven decisions, with production-ML and data-quality discipline on top."
- **Reframing map:**
  - SW-7 Data Mesh self-serve data products → "durable, self-serve data products consumed across the org" (the JD's core ask)
  - SW-1 Teradata/Oracle→AWS ETL + Glue/Airflow/Redshift → "data pipeline design + dimensional modeling on a cloud data platform"
  - SW-6 PySpark/Data Lake → "distributed data processing (Spark; transferable to DataFlow/Flume)"
  - BS-4 ELK/Grafana/Prometheus + anomaly detection → "automated validation, data quality + reliability monitoring"
  - SW-4 dashboards + PO/stakeholder → "data products for stakeholders + cross-functional partnering" (Analytics-Eng bridge)
  - BS-1 production ML → "ML for production workflows" (preferred qual)
  - Component/Application Owner → "high autonomy, own projects conception→impact"
- **Emphasize:** self-serve data products (flagship), pipeline design + dimensional modeling, Spark, data quality/validation/reliability monitoring, stakeholder partnering, autonomy, production ML
- **Downplay:** SRE/on-call framing (less central here than Kraken), crypto/Web3 (NOT relevant — drop crypto skills group), semiconductor jargon (keep ML + data-quality angle), pure DevOps/K8s emphasis (secondary here)
- **CL hooks:** (1) Shopping Graph as a data product at 60B-product scale ↔ his self-serve governed data products; (2) "bridge SWE/DE/DS" = his cross-functional Component/App Owner role; (3) data quality + reliability monitoring (his observability/governance work) for AI-powered Shopping experiences; (4) Zürich Merchant Data Science team, local to Bern.
- **User directives:** Honest tool-bridging (NO BigQuery/Dataflow/Flume by name). Clarify level/comp at recruiter stage (comp clears bar even at L4). Drop crypto framing (not relevant to this JD).

## Critique Context
- **Reviewer persona:** A Google L5/L6 Data Engineer tech lead or DE manager on Merchant Data Science (Zürich). Cares about real pipeline + dimensional-modeling depth, data-product thinking (self-serve, durable, consumed at scale), data-quality/validation rigor, autonomy, and Google-scale readiness. Bored by buzzwords and BI-dashboard-only profiles. Probes whether candidate has designed data models, not just moved data.
- **Competitive landscape:** Obvious fit = DE with BigQuery/Dataflow/dbt + e-commerce/marketplace data, possibly ex-FAANG, fluent in dimensional modeling at scale. Dennis's edge: self-serve data-product/Data-Mesh platform thinking at telco scale, production ML, data-quality/reliability discipline, and broad multi-industry delivery. Their edge: GCP/BigQuery by name, Google-scale, marketplace domain.
- **Domain vocabulary (insider):** Shopping Graph, data products, self-serve, dimensional modeling, fact/dimension tables, pipeline DAG, data quality SLAs, validation frameworks, freshness/completeness, lineage, Flume/Dataflow/BigQuery, Dremel/ZetaSQL, batch + streaming, idempotency, backfill.

## Cover Letter Plan
- **Institution type:** Industry (big-tech, data-engineering audience)
- **Paragraph count:** 3-4 paragraphs, ~270 words
- **P1 hook:** Shopping Graph as a 60B-product data product + Merchant Data Science "bridge SWE/DE/DS" ↔ my self-serve governed data products at telco scale; Zürich team local fit
- **P2-P3 evidence:** P2 = pipeline design + dimensional modeling + self-serve data products (SW-1, SW-7, SW-6) consumed across teams. P3 = data quality/validation + reliability monitoring (BS-4) + production ML (BS-1) + cross-functional stakeholder ownership (SW-4, BS-3)
- **Domain pivot:** AWS/Spark/Airflow honestly transferable to Google's Flume/DataFlow/BigQuery — state the analog confidently, never claim the Google-internal tools by name
- **Jargon level:** Technical (DE audience)
- **"Why them" hook:** Builds data products others depend on to decide — wants to do it at Shopping-Graph scale on the Zürich team

## Bullet Plan (proposed 2026-06-15 — 17 variable bullets, proven QuantCo 2-page fill)

Drop SW-5 (security — not relevant, avoids "3 years" correction). Drop crypto skills group → replace with BI/Analytics group. No US immigration line (Zürich target). TAF 2022 Spotfire talk → CL hook + interview bridge (Option A) unless user wants its own bullet (Option B).

### Position 1 — Swisscom · Staff Data, Analytics & AI Engineer (6 bullets)
| # | ID | Achievement | Variant |
|---|----|------------|---------|
| 1 | SW-7 | LEAD — self-serve governed data products + metadata on AWS Data Mesh, consumed across teams | 2L |
| 2 | SW-1 | AWS migration of Teradata/Oracle ETL + dimensional modeling (Glue/Athena/Iceberg/Redshift/Airflow) | 2L |
| 3 | SW-2 | Component Owner Fulfillment ETL (Oracle/Kafka→Teradata), data governance + quality + SLA | 2L |
| 4 | SW-4 | B2B data products + dashboards (Tableau/QuickSight) + stakeholder + automation + RCA | 2L |
| 5 | SW-6 | PySpark distributed data processing in Data Lake | 2L |
| 6 | SW-3 | Python apps on Kubernetes + GitLab CI/CD (engineering credibility) | 2L |

### Position 2 — Bosch · (Senior) Data Engineer (4 bullets)
| # | ID | Achievement | Variant |
|---|----|------------|---------|
| 1 | BS-1 | Production ML inference into 24/7 fab (Docker/K8s/Ansible) — "ML for production workflows" | 2L |
| 2 | BS-3+BS-5 | Application Owner + CO-OWNED TIBCO Spotfire analytics platform + Defect Mgmt System; C# extensions; SLOs, training | 2L |
| 3 | BS-4 | ELK/Grafana/Prometheus anomaly detection — data quality + reliability monitoring | 2L |
| 4 | BS-2 | Multi-language data services over Oracle + Hadoop/Impala for analysis teams | 2L |

### Position 3 — Fraunhofer (2) · Position 4 — Vizrt (2) · Position 5 — Generali (3)
FC-1 (Jenkins CI/CD from zero + SCEDAS), FC-3 (Express.js/Docker microservices) | VZ-1 (distributed Python/C++ backend, CNN/BBC), VZ-2 (test suite + CI/CD gates) | GN-1 (BDD + Jenkins ownership), GN-3 (Java/J2EE), GN-2 (UIPath RPA)

**Skills (4-3-2-2-2):** (1) Data Engineering & Pipelines, (2) Cloud Platform & Infrastructure, (3) BI/Analytics & Data Consumption (Spotfire/Tableau/QuickSight/Jupyter), (4) Programming & Databases, (5) Certifications.
**Summary headline:** "Senior Data Engineer | Python · SQL · AWS · Spark | Data Products, Dimensional Modeling & Pipeline Ownership"

**TAF 2022 decision (pending user):** Option A = combine BS-3+BS-5 (1 bullet) + TAF talk in CL/interview (RECOMMENDED, keeps DE focus, 17 bullets). Option B = Spotfire+TAF its own bullet (Bosch→5), drop GN-2 to stay at 17.

## Output Files
- Resume: `output/Google_Senior_Data_Engineer/e2e_google_senior_data_engineer_resume.tex` (+ .pdf, 2 pages)
- Cover Letter: `output/Google_Senior_Data_Engineer/e2e_google_senior_data_engineer_cover_letter.tex` (+ .pdf, 1 page, 299 words). Hooks verified 2026-06-15: (1) Shopping Graph 60B+ listings — confirmed (Google I/O 19 May 2026, VP Vidhya Srinivasan); (2) TAF 2022 talk "Understanding Spatial Variability in Semiconductor Products with Spotfire Map Charts" by Mark Herrmann + Dennis Thießen (Bosch Dresden) — confirmed (community.spotfire.com/articles/spotfire/tibco-analytics-forum-2022). NOTE: moderncv needs `\microtypesetup{expansion=false}` or fontawesome icons fail compile.
- Critique: CURRENT — **85.5/100** (2026-06-15; baseline 83.0 pre-edit). Strong Tier-1 DE fit; SW-7 self-serve data products ≈ team charter verbatim; honest GCP-tool bridges; AI scan clean; CL 1pp. **Tier-1 + both Tier-2 fixes APPLIED & re-verified:** (1) migration claim re-scoped in resume B2 + CL P2 (Scope-Discipline error cleared in both docs); (2) B4 now reads "on time and in scope" (project-delivery preferred qual); (3) B5 "distributed computing"→"distributed data processing". Resume 2pp / CL 1pp clean compile, B2 216/B4 190/B5 206 chars (≤218). **SENT 2026-06-15.** Hard ceiling ~87 (no GCP/BigQuery-by-name, no marketplace domain — not closable). Open item at recruiter stage: clarify L4/L5 + confirm comp clears 180k+.
- **ADVANCED 2026-06-17 — invited to 30-min Google Hiring Assessment.** Resume cleared the recruiter screen. Next action: complete the online assessment within the deadline in the invite email. Confirm exact format from the email (Google Hiring Assessment is typically online + timed; for DE roles expect SQL + possibly Python/data-modeling and/or situational-judgment questions — verify, do not assume). Prep focus: SQL (window functions, joins, aggregation), dimensional modeling, basic Python/data manipulation.

- **PASSED HIRING ASSESSMENT 2026-06-20.** Pass retained as a separate candidate-history signal; prior tracker record states 24-month validity.
- **CLOSED — NOT PROCEEDING 2026-07-24.** Google Careers status changed three days before 2026-07-27; no interview followed the assessment.

## Status
- Phase 0: **DONE**
- Phase 1: **DONE** (17-bullet Option A package)
- Phase 2 Resume: **DONE** (2 pages, clean compile)
- Cover Letter: **DONE** (1 page, 299 words, clean compile)
- Critique: **CURRENT — 85.5/100**
- Application: **CLOSED — NOT PROCEEDING 2026-07-24** (applied 2026-06-15; assessment passed 2026-06-20; no interview)
- Google reapplication rule: 90-day wait applies to the **same job**; other Google roles remain eligible, subject to the maximum of 3 applications in a rolling 30-day window. Official source: https://support.google.com/googlecareers/answer/6095391
- **Next:** Done. Retain the assessment pass in candidate history and target materially different, strong-fit Google requisitions.