% Citadel Securities — Platform Engineer, Research Platform Engineering (New York, Miami, Zurich)
% Profile: International Tech, English, 2 pages. Generated 2026-08-26.
% Generated from resume_builder/canonical/claims.json + normalized experience files.
% Several tools named in the JD are non-canonical and are deliberately absent from this
% document; they are listed and bridged in the session file's Gap Assessment. Do not
% reintroduce them here, not even in a comment -- the validator scans raw file text.
% JD qualifications 3 and 7 are disjunctive, so Python and AWS satisfy them alone.
% PP-1 appears only under an explicitly labelled Personal Project heading; claims.json
% forbids calling it distributed/production/multi-user or quoting ANY performance figure.

\documentclass{resume}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{lmodern}
\usepackage[a4paper,left=0.65in,right=0.65in,top=0.55in,bottom=0.55in]{geometry}
\usepackage{xcolor}
\usepackage{hyperref}
\hypersetup{hidelinks}
\usepackage[version=4]{mhchem}
\usepackage{fancyhdr}
\pagestyle{fancy}
\fancyhf{}
\renewcommand{\headrulewidth}{0pt}
\fancyfoot[R]{\small \thepage/\pageref{LastPage}}

\name{Dennis Thiessen, M.Eng.}
\headline{Staff Data Engineer $\vert$ Production Data Platforms on AWS $\vert$ Kafka $\cdot$ Kubernetes $\cdot$ Spark}
\contactline{Bern, Switzerland $\vert$ \href{mailto:dennis@thiessen.io}{dennis@thiessen.io} $\vert$ +41 795 955 585 $\vert$ \href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}}

\begin{document}

\begin{rSection}{Summary}
Staff data engineer who builds and runs the pipelines and governed data products that analytics and AI consumers depend on, on AWS and Kubernetes at Swisscom, with Component Ownership covering data quality, incidents and on-call duty. Earlier work at Bosch tuned query performance over Oracle and Hadoop/Impala for semiconductor analysis teams and moved containerized ML inference into a continuously operating fab.
\end{rSection}

\begin{rSection}{Technical Skills}
\skillline{Programming}{Python, SQL, PySpark; Java and C\# (professional, historical); C++ (limited historical use)}
\skillline{Distributed systems}{Apache Kafka, Apache Airflow, Spark/PySpark, distributed databases (Teradata, Redshift, Athena/Iceberg), ETL/ELT, data modelling, query performance tuning, Oracle, Hadoop/Impala}
\skillline{Cloud and platform}{AWS (S3, Glue, Athena/Iceberg, Redshift, Lambda, Step Functions), CloudFormation, Kubernetes, Docker, GitLab CI/CD, Ansible}
\skillline{Reliability and operations}{Production operation and on-call, SLOs, incident and root-cause analysis; Grafana, Prometheus, ELK (proof of concept)}
\skillline{Trading domain (self-directed)}{Systematic/quantitative trading concepts, factor construction, backtesting -- personal project and coursework only, no professional experience}
\skillline{Certifications}{AWS Solutions Architect -- Associate; Data Engineering with AWS; iSAQB CPSA-F; ITIL Foundation}
\end{rSection}

\begin{rSection}{Professional Experience}

\begin{rSubsection}{Swisscom (Schweiz) AG}{Oct 2023 -- Present}{Staff Data, Analytics \& AI Engineer (promoted from Senior, Apr 2025)}{Bern, Switzerland}
\item Build and model governed data products within Swisscom's company-wide Data Mesh, from source-system ingestion through to the metadata and lineage in Atlassian Compass that keep each product discoverable across its lifecycle.
\item Component Owner for business-critical Fulfillment ETL -- a distributed system spanning Oracle, Kafka, Python and Teradata -- accountable for its data quality, governance and on-call operation.
\item Migrated owned Fulfillment and Product Analysis pipelines onto Swisscom's AWS platform -- transformation and storage on Glue, Athena with Apache Iceberg and Redshift, orchestration in Airflow -- contributing to the wider company migration programme.
\item Develop and operate Python data applications on Kubernetes, delivered through GitLab CI/CD from automated test to production support.
\item Translate stakeholder and product-owner requirements into delivered data products; run root-cause analysis in 2nd- and 3rd-level support.
\end{rSubsection}
\end{rSection}

\newpage

\begin{rSection}{Professional Experience (continued)}
\begin{rSubsection}{Robert Bosch Semiconductor Manufacturing Dresden GmbH}{Feb 2020 -- Dec 2022}{Senior Engineer, Data Analysis (Data Engineering)}{Dresden, Germany}
\item Developed data services in Python, Java and C\# over Oracle and Hadoop/Impala, tuning query performance for analysis teams working with semiconductor defect records, wafer inspection images and process-control electrical parameters.
\item Integrated containerized ML inference with Docker, Kubernetes and Ansible into a continuously operating semiconductor fab, replacing manual image-based defect classification.
\item Built an anomaly-detection proof of concept on Elasticsearch, Logstash, Kibana and Kafka, with Grafana and Prometheus monitoring for continuously running manufacturing systems.
\item Served as Application Owner for analytics applications and their upstream pipelines, defining SLOs and coordinating vendors, user training and documentation.
\end{rSubsection}

\begin{rSubsection}{Fraunhofer CML}{Sep 2018 -- Oct 2019}{Research Software Engineer}{Hamburg, Germany}
\item Developed containerized microservices for the MISSION maritime research data-exchange platform.
\end{rSubsection}

\begin{rSubsection}{Vizrt}{Jul 2017 -- May 2018}{Test Automation / DevOps Engineer}{Bergen, Norway}
\item Contributed Python and C++ engineering to a distributed video-transcoding backend.
\end{rSubsection}

\begin{rSubsection}{Generali Deutschland Informatik Services GmbH}{May 2015 -- Jun 2017}{IT Consultant}{Hamburg, Germany}
\item Introduced BDD test automation through a proof of concept and held technical responsibility for the suite and its Jenkins jobs.
\end{rSubsection}

\end{rSection}

\begin{rSection}{Personal Project}
\compactentry{Equity Signal Platform -- self-hosted, single-user}{Personal project}
Ingests daily US-equity prices, fundamentals and LLM-assisted sentiment; runs one long-only cross-sectional momentum book (residual 12-1 momentum, ATR stop and trail, at most 15 concurrent positions) through scheduled scan and backtest pipelines; surfaces the gated setups in a web dashboard and Telegram alerts. Built end to end as a personal system, not a production or multi-user service. Supporting coursework: Udacity \textit{AI for Trading} nanodegree (factor construction, alpha signals, backtesting).
\end{rSection}

\begin{rSection}{Education}
\compactentry{M.Eng. Computer Aided Engineering (Software Design \& Engineering), Universität der Bundeswehr München}{Apr 2012 -- Oct 2013}
Thesis at Tongji University, Shanghai: \textit{Development of a Web-Based Remote Fault Diagnosis System}; grade 1.0.

\compactentry{B.Eng. Information and Telecommunication Technologies, Universität der Bundeswehr München}{Oct 2009 -- Oct 2012}
\end{rSection}

\end{document}
