# Bundle: Analytics Engineer

> Target employers: Data-driven companies, BI/analytics teams
> Tier: 2 — strong with targeted emphasis
> Config key: bundle_analytics_engineer.md

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## S1: Role Profile & Priority Matrix

**Positioning:** Dennis bridges the gap between pure data engineering and analytics — he has built and owned the pipelines *and* delivered the data products and dashboards that stakeholders actually use. His B2B analytics delivery at Swisscom (Switzerland's largest telco), Application Owner experience at Bosch (semiconductor analytics platforms), and domain depth in semiconductor data (defect management, parameter testing, process analysis) make him a credible Analytics Engineer candidate who understands both the technical infrastructure and the analytical outcomes.

**Key differentiator for this role type:** Most Analytics Engineer candidates are either BI-first (lighter on engineering) or DE-first (lighter on business context). Dennis has both: owned pipelines AND delivered B2B data products AND held application ownership for analytics platforms.

### Priority Matrix

| Priority | Achievement IDs | Rationale |
|----------|----------------|-----------|
| HIGH | SW-4, SW-1, SW-2, BS-3 | Core analytics signals: B2B products, AWS data infra, pipeline ownership, analytics platform owner |
| MED | SW-3, SW-6, BS-1 (semi JDs), BS-2, FC-1 | Supporting: K8s delivery, PySpark, ML analytics at Bosch, data service depth |
| LOW | SW-5, BS-4, FC-2, FC-3, VZ-1, VZ-2, GN-1, GN-2 | Infrastructure/testing — not primary signal for analytics audience |

**2-page resume bullet allocation (typical):**
- Swisscom: 3–4 bullets (SW-4, SW-2, SW-1, SW-3)
- Bosch: 2–3 bullets (BS-3, BS-2; +BS-1 for semi JDs)
- Fraunhofer: 1 bullet (FC-1 compressed or omit)
- Vizrt: 1 bullet (combined or omit)
- Generali: 1 bullet (GN-1 or omit)

---

## S2: Summary Guide

**Headline pattern:**
> "Analytics Engineer | Python · SQL · AWS · Teradata | Data Products, Pipeline Ownership & Stakeholder Analytics"

**Building blocks:**
- "data products for business stakeholders" or "B2B analytics delivery"
- "end-to-end pipeline ownership" (show both build and deliver)
- "analytics platform ownership" (App Owner at Bosch)
- "AWS cloud-native stack" (Athena, Glue, Redshift — standard analytics stack)
- Semiconductor domain angle (for semi JDs): "semiconductor manufacturing analytics — defect management, parameter testing, process analysis"

**Tone:** Pragmatic engineer who cares about outcomes, not just pipelines. Business-aware. Bridges technical depth and analytical impact.

**Avoid:**
- Leading with ML/AI framing (not the primary signal here)
- Heavy infrastructure language (Kubernetes, Ansible) unless JD asks for it
- Overplaying test automation background

---

## S3: Achievement Reframing Map

| ID | Default Framing | This Role's Framing | Key Metric / Signal |
|----|----------------|--------------------|--------------------|
| SW-4 | B2B products + automation | **Lead bullet** — "Delivered data products, analyses and dashboards for B2B stakeholders; drove automation of recurring technical workflows" | Stakeholder-facing delivery |
| SW-2 | Component Owner ETL | "Owned Fulfillment ETL pipelines (Oracle/Kafka → Teradata) — ensuring data availability for downstream analytics with SLA accountability" | Pipeline → analytics link |
| SW-1 | AWS migration | "Migrated pipelines in his owned domains onto Swisscom's AWS platform (S3, Glue, Athena/Iceberg, Redshift, Airflow)" | Scoped delivery on a current analytics stack; no unverified scale claim |
| BS-3 | Application Owner | "Application Owner for semiconductor data analysis platforms — defined SLOs, trained users, managed vendor relationships and stakeholder expectations" | Ownership + analytics platform |
| BS-2 (generic) | Data services | "Built data services supplying analysis teams with on-demand structured access to manufacturing process data" | Analytics enablement framing |
| BS-2 (semi JD) | Data services | "Built data services enabling Defect Management, Parameter Testing and Process Analysis teams with on-demand data access" | Semiconductor domain specificity |
| BS-1 (semi JD) | ML inference | "Automated image-based defect classification for semiconductor production — enabling defect management analytics without manual inspection" | Semi domain + analytics outcome |
| SW-3 | K8s/CI/CD | "Deployed and operated Python data applications on Kubernetes with GitLab CI/CD automation" | Engineering credibility signal |

---

## S4: Skills Guide

**Bold tools (resume Technical Skills section):**
Python, SQL (Oracle · Teradata · Athena), AWS (S3 · Glue · Athena · Redshift), PySpark, Airflow

**Must-include skills (ATS match):**
- Python, SQL, data modeling
- AWS (Athena, Glue, Redshift, S3), Apache Iceberg
- Apache Airflow, ETL/ELT
- Teradata, Oracle DB
- PySpark, Pandas
- Grafana or data visualization tools (Plotly/Matplotlib)

**Nice-to-have (include if JD mentions):**
- Apache Kafka (pipeline depth signal)
- dbt (not evidenced — do NOT claim; if JD requires, flag to user)
- Tibco Spotfire (niche — only for Spotfire-specific JDs)
- Kubernetes (engineering depth signal if JD asks for it)

**Omit:**
- ELK Stack, Prometheus, Loki (infra monitoring — not analytics signal)
- Ansible, IaC/CloudFormation (infra — not analytics signal)
- RPA/UIPath, BDD, Selenium (testing — irrelevant)

**Certifications to highlight:**
- AWS Certified Solutions Architect – Associate → supporting (confirms AWS stack)
- Data Engineering with AWS (Udacity) → directly relevant
- iSAQB CPSA Foundation Level → minor supporting signal

---

## S5: Cover Letter Guide

**Institution type:** Industry — data-driven product company, BI/analytics team, or data consultancy

**Opening hook pattern (generic):**
> "At Swisscom, I own both sides of the analytics equation: I build and operate the Fulfillment ETL pipelines that feed our Teradata data warehouse, and I deliver the data products and dashboards that our B2B teams rely on for decision-making. [Tie to their specific analytics use case]."

**Opening hook pattern (semiconductor JD):**
> "My experience at Bosch Semiconductor sits at the intersection of data engineering and semiconductor manufacturing analytics — I built the pipelines that fed defect management and parameter testing teams, owned the analytics platforms those teams relied on, and deployed ML-based image classification that automated defect analysis on the production line. I'd bring that same domain depth and ownership mentality to [Company]."

**Key narrative thread:**
1. **Analytics platform ownership** — App Owner at Bosch: not just building queries, but owning the analytics software that teams depend on
2. **Pipeline-to-insight chain** — Fulfillment Component Owner at Swisscom: show the full chain from raw Oracle/Kafka data → Teradata DWH → B2B analytics
3. **Cloud analytics stack** — hands-on migration of owned-domain pipelines using Athena/Iceberg/Glue within a wider programme
4. **Semiconductor domain** (for semi JDs): Defect Management + Parameter Testing + Process Analysis — rare domain expertise in an Analytics Engineer candidate

**"Why them" angle to research:**
- What business domain are their analytics teams serving? Map to Swisscom (telecom) or Bosch (manufacturing) experience
- What is their analytics stack? AWS-heavy roles can use SW-1 directly when company-wide ownership is not implied
- Do they use dbt? Flag if so — not in your stack, but Airflow/Glue is adjacent

**Avoid:**
- Leading with infrastructure or DevOps framing
- Overplaying ML/AI angle (secondary for analytics audience)
- Mentioning ITIL, SCEDAS, or maritime research context
