Bundle: Data Platform / Infra

Target employers: Cloud-first companies, AWS-heavy orgs Tier: 3 — viable with careful framing Config key: bundle_data_platform.md

S1: Role Profile & Priority Matrix

Positioning: Dennis's data platform and infrastructure experience is woven throughout his career rather than being a dedicated "platform engineer" role — but the evidence is substantive: Kubernetes ownership at two employers, AWS migration with CloudFormation/IaC, GitLab CI/CD automation, Docker containerization of ML workloads, observability stack (ELK + Grafana + Prometheus), and 3 consecutive years as Swisscom Security Champion (DevSecOps). Position as "Data Engineer with strong platform and infrastructure ownership" rather than a dedicated Platform/SRE/DevOps role.

Note on Tier 3: This bundle is viable but slightly less natural than Tier 1/2. The gap is: Dennis doesn't have a dedicated platform engineering title, and his infrastructure work is in service of data pipelines rather than standalone infrastructure. Frame accordingly — emphasize that his platform skills are production-proven, not academic.

Priority Matrix

Priority	Achievement IDs	Rationale
HIGH	SW-3, SW-1, SW-2, BS-1, BS-2, BS-3, BS-4, SW-5	K8s/GitLab, AWS/IaC, pipeline ownership, ML containerization, data services, ELK observability, DevSecOps
MED	SW-4, SW-6, FC-1, FC-3, VZ-2, BS-5	Automation, PySpark, CI/CD initiative, microservices, quality gates
LOW	FC-2, VZ-1, GN-1, GN-2, CA-1	Non-platform signals

2-page resume bullet allocation (typical):

Swisscom: 3–4 bullets (SW-3, SW-1, SW-2, SW-5)
Bosch: 3 bullets (BS-1, BS-2 or BS-3, BS-4)
Fraunhofer: 1 bullet (FC-1 — CI/CD initiative)
Vizrt: 1 bullet (VZ-2 — quality gates in CI/CD)
Generali: 1 bullet (GN-1 or omit)

S2: Summary Guide

Headline pattern:

"Data Platform Engineer | Kubernetes · AWS · Kafka | Cloud-Native Data Infrastructure, IaC & DevSecOps"

Building blocks:

"cloud-native data infrastructure" or "data platform ownership"
"Kubernetes-based containerized pipeline deployment"
"AWS IaC (CloudFormation)" — infrastructure-as-code signal
"AWS migration" — hands-on cloud platform experience
"DevSecOps / Security Champion" — security-aware platform engineer
"ELK + Grafana + Prometheus observability stack"

Tone: Infrastructure-minded engineer who thinks about reliability, observability, and security — not just data throughput. Platform thinking embedded in data work.

Avoid:

Leading with analytics or BI framing
Overemphasizing test automation background
Positioning as SRE or pure DevOps (the role was data engineering with platform ownership)

S3: Achievement Reframing Map

ID	Default Framing	This Role's Framing	Key Metric / Signal
SW-3	K8s + GitLab	Lead bullet — "Deployed and operated Python data applications on Kubernetes with GitLab CI/CD; drove infrastructure automation in agile DevOps team"	K8s + CI/CD ownership = core platform signal
SW-1	AWS migration	"Migrated legacy ETL stack to cloud-native AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation) — full IaC stack provisioned via CloudFormation"	CloudFormation/IaC + full AWS service breadth
SW-2	Component Owner	"Owned Fulfillment ETL pipelines (Oracle/Kafka → Teradata) — platform reliability, Data Governance compliance, 2nd/3rd-level support and on-call duty"	Platform SLA + on-call = reliability engineer signal
BS-1	ML inference	"Containerized and orchestrated ML inference (Docker, K8s, Ansible) into 24/7 semiconductor production — zero-downtime constrained deployment"	Production-grade containerization under hardest constraints
BS-4	ELK PoC	"Designed and delivered observability stack: ELK + Kafka, Grafana dashboards, Prometheus metrics, Loki log aggregation — full monitoring suite for manufacturing infrastructure"	Full observability stack implementation
SW-5	Security Champion	"Swisscom Security Champion ×3 (2023–2026) — DevSecOps ownership, security compliance, risk monitoring and deviation tracking for Data Lake team"	Security ownership in platform context
BS-2	Data services	"Built multi-language data services (Python/Java/C#) over OracleDB and Hadoop/ImpalaSQL — platform-layer data access for semiconductor analysis teams"	Enterprise DB + Hadoop infrastructure
BS-3	App Owner	"Application Owner for semiconductor analytics platform — SLOs, reliability, vendor management, on-call coverage"	Platform SLA ownership
FC-1	CI/CD initiative	"Independently introduced Jenkins CI/CD pipeline with quality gates at Fraunhofer CML — first build automation adopted by the research team"	Initiative: built CI/CD from zero

S4: Skills Guide

Bold tools (resume Technical Skills section): Kubernetes, Docker, AWS (S3 · Glue · Athena · Redshift · CloudFormation), Kafka, GitLab CI/CD

Must-include skills (ATS match):

Kubernetes, Docker, Ansible
AWS (S3, Glue, Athena, Redshift, CloudFormation, Airflow), Apache Iceberg
GitLab CI/CD, Jenkins
Kafka, Apache Airflow
Python, SQL
ELK Stack, Grafana, Prometheus
IaC / CloudFormation
DevSecOps

Nice-to-have (include if JD mentions):

Terraform (not evidenced — do NOT claim; flag if JD requires)
Loki (log aggregation — from Bosch PoC)
PySpark (distributed processing on platform)
Ansible (Bosch ML orchestration)
Oracle DB, Teradata (enterprise data platform experience)

Omit:

BDD, Selenium, HP Quality Center, UIPath (testing — irrelevant)
Tibco Spotfire, SAP BODS (application tools — irrelevant)
RPA/Camunda (process automation — irrelevant)

Certifications to highlight:

AWS Certified Solutions Architect – Associate → HIGH (platform credibility, architecture knowledge)
Data Engineering with AWS → supporting
iSAQB CPSA Foundation Level → MED (software architecture — relevant for platform design decisions)

S5: Cover Letter Guide

Institution type: Cloud-first tech company, scale-up with AWS-heavy stack, enterprise platform team, or data infrastructure consultancy

Opening hook pattern:

"Across my career at Swisscom and Bosch, I've owned data infrastructure at two ends of the spectrum: migrating Swisscom's legacy ETL stack to a cloud-native AWS platform (CloudFormation, Glue, Athena with Iceberg, Airflow) while operating Kubernetes-deployed Python applications with GitLab CI/CD — and containerizing ML inference into a 24/7 semiconductor production line at Bosch using Docker, Kubernetes, and Ansible. In both cases, the infrastructure had to be production-grade with no tolerance for downtime. [Tie to their platform challenge]."

Key narrative thread:

Production Kubernetes — SW-3 + BS-1: K8s at two employers, in different contexts (data apps at Swisscom, ML inference at Bosch). Cross-employer K8s ownership is a strong signal.
Full AWS platform stack — SW-1: Not just using one AWS service — migrating an entire ETL infrastructure to AWS with CloudFormation/IaC shows platform-level thinking.
Observability initiative — BS-4: Self-initiated ELK + Prometheus + Grafana PoC shows platform engineer mindset (monitoring is not optional).
Security ownership — SW-5: Security Champion ×3 = DevSecOps embedded in platform work, not an afterthought.

"Why them" angle to research:

What is their cloud stack? If AWS-heavy → your SAA cert + migration experience is directly relevant
Do they use Kubernetes in production? → Cross-employer K8s experience is the signal
Are they building their data platform from scratch vs. maintaining existing? → Tailor SW-1 (migration) vs. BS-4 (observability initiative) accordingly
Terraform vs. CloudFormation? → Note that your experience is CloudFormation; Terraform familiarity may need bridging