172 lines
13 KiB
TeX
172 lines
13 KiB
TeX
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%----------------------------------------------------------------------------------------
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% HEADER
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%----------------------------------------------------------------------------------------
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\name{Dennis Thiessen, M.Eng.}
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\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}}
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\address{dennis@thiessen.io \\ +41 795 955 585}
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\address{Bern, Switzerland $\vert$ German citizen $\vert$ Open to relocation to Zurich}
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\address{{Staff Data Engineer $\vert$ NLP \& Computer Vision $\cdot$ Airflow $\cdot$ Agentic Workflows $\vert$ AWS $\cdot$ Python}}
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\begin{document}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% SUMMARY
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Summary}
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Data and ML engineer with 10+ years building production data pipelines --- Fraunhofer \textbf{NLP} research, Bosch \textbf{computer vision} in a 24/7 semiconductor fab, and Swisscom telecom-scale ETL at petabyte scale. At Swisscom, own the \textbf{AWS} data platform (\textbf{Airflow}, Glue, Athena, \textbf{PySpark}) processing large-scale data for ML and analytics. Expert in \textbf{Python}; designed and implemented agentic workflows using \textbf{LangChain} and custom GPTs to automate engineering processes. M.Eng.\ (thesis grade 1.0) in neural network-based fault diagnosis. German native, fluent English.
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% TECHNICAL SKILLS — Format C, 5 groups (4-3-2-2-2 = 13 lines)
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Technical Skills}
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\begin{skillgroup}{Machine Learning \& AI}
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\skilldash{\textbf{NLP}, \textbf{computer vision}, deep learning, ML inference deployment, generative AI / LLMs, \textbf{agentic workflows}}
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\skilldash{\textbf{LangChain}, custom GPT development, \textbf{PyTorch}, TensorFlow/Keras (IBM cert), Scikit-learn, Spark ML}
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\skilldash{Multi-domain data processing (tabular, image, text, video), speech recognition, image classification, anomaly detection}
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\skilldash{Statistical modeling, time-series analysis, quantitative ML, data quality, model training support, data preprocessing}
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\skilldash{Human-in-the-loop data workflows, ML dataset curation, annotation pipeline support, data quality validation}
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\skilldash{Synthetic data preprocessing, multi-modal dataset pipelines, model training data at petabyte scale}
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\end{skillgroup}
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\begin{skillgroup}{Data Engineering \& Orchestration}
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\skilldash{\textbf{Apache Airflow}, Apache Kafka, \textbf{PySpark} / Apache Spark, \textbf{Databricks}, Apache Iceberg, Hadoop/ImpalaSQL}
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\skilldash{\textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Lambda, \textbf{Airflow}, CloudFormation), Teradata DWH, OracleDB}
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\skilldash{ETL/ELT pipeline design, data modeling, data governance, SQL (Oracle, Impala, Teradata, Postgres), NoSQL}
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\skilldash{Data pipeline monitoring, SLA compliance management, batch and stream processing, data lineage, data versioning}
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\end{skillgroup}
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\begin{skillgroup}{Cloud \& Container Infrastructure}
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\skilldash{\textbf{Docker}, \textbf{Kubernetes}, Ansible, GitLab CI/CD, Jenkins, Infrastructure as Code, DevSecOps, build automation}
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\skilldash{ELK Stack (Elasticsearch, Logstash, Kibana), Grafana, Prometheus, Loki, monitoring, log aggregation, alerting}
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\skilldash{AWS Lambda, CloudWatch, ECR, ECS, Step Functions, SQS, SNS, event-driven architectures, serverless}
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\end{skillgroup}
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\begin{skillgroup}{Programming Languages \& Frameworks}
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\skilldash{\textbf{Python} (expert), \textbf{Java} (strong), C++, C\#, JavaScript, SQL, Flask/FastAPI, Express.js, .NET/Entity Framework}
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\skilldash{Pandas, NumPy, SQLAlchemy, Matplotlib, Bash, Git, pytest, Agile/Scrum, technical documentation}
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\skilldash{Jupyter Notebooks, dbt, shell scripting, code review, unit testing, software design patterns}
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\end{skillgroup}
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\begin{skillgroup}{Certifications}
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\skilldash{AWS Certified Solutions Architect -- Associate (2024, active), Data Engineering with AWS (Udacity, 2026)}
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\skilldash{IBM AI Engineering Specialization, AI for Trading Nanodegree (Udacity, 2021), iSAQB CPSA-F (2016)}
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\end{skillgroup}
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% PROFESSIONAL EXPERIENCE
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Professional Experience}
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% --- Swisscom (Oct 2023 -- Present) — 4 bullets: SW-2, SW-1, SW-GenAI, SW-4 ---
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\begin{rSubsection}{ML Data Pipelines, Agentic Workflows \& Cloud Infrastructure}{\textcolor{black!60}{Oct 2023 -- Present}}{Staff Data, Analytics \& AI Engineer, Swisscom (Schweiz) AG}{Bern, Switzerland}
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\item Owned Fulfillment and Product Analysis ETL pipelines (Oracle, \textbf{Kafka} to Teradata DWH in \textbf{Python}) as component owner, enforcing data governance and SLA compliance for business-critical production data flows at scale.
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\item Migrated legacy Teradata/Oracle ETL stack to \textbf{AWS} (S3, Glue, \textbf{Airflow}, Athena/Iceberg, Redshift, CloudFormation), enabling scalable serverless data processing for ML and analytics at telecom scale.
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\item Designed and implemented agentic \textbf{LangChain} workflows with domain-specific GPT knowledge bases at Swisscom, automating code review, documentation, and pipeline troubleshooting to cut manual engineering effort.
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\item Delivered self-service data products, analyses and dashboards for B2B stakeholders; drove \textbf{Python} process automation and 3rd-level root cause analysis to maintain reliable data platform operations.
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\item Deployed and operated \textbf{Python} data applications on \textbf{Kubernetes} clusters with GitLab CI/CD automation, owning the containerized delivery lifecycle from build and test to production rollout in an agile DevOps team.
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\item Applied \textbf{PySpark} and distributed computing within the Swisscom Data Lake platform, extending \textbf{Python} pipeline capabilities to large-scale batch workloads for Fulfillment and Product Analysis data.
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\end{rSubsection}
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% --- Bosch (Feb 2020 -- Dec 2022) — 4 bullets: BS-1, BS-2, BS-3, BS-4 ---
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\begin{rSubsection}{Computer Vision \& ML Deployment in Semiconductor Manufacturing}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{(Senior) Data \& ML Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany}
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\item Deployed \textbf{ML inference} (\textbf{Docker}, Kubernetes, Ansible) into a 24/7 semiconductor fab, automating \textbf{computer vision}-based defect classification and replacing manual inspection across 300mm production lines.
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\item Built data services in \textbf{Python}, Java and C\# over OracleDB and Hadoop/ImpalaSQL, supplying semiconductor analysis teams with structured access to defect management and process optimization data.
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\item Served as Application Owner for the semiconductor analytics suite and upstream data pipelines, defining SLOs, managing vendors, and delivering user training and documentation across fab operations teams.
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\item Delivered anomaly detection PoC using ELK Stack and \textbf{Kafka} (\textbf{Docker}) with Grafana, Prometheus and Loki monitoring, demonstrating centralized real-time alerting for 24/7 semiconductor infrastructure.
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\item Built C\# analytical extensions for Tibco Spotfire at Bosch Semiconductor, delivering custom data visualization and querying capabilities to support semiconductor process engineers in wafer defect analysis.
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\end{rSubsection}
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% --- Fraunhofer (Sep 2018 -- Oct 2019) — 3 bullets: FC-2, FC-1, FC-3 ---
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\begin{rSubsection}{Applied NLP/ML Research \& Software Engineering}{\textcolor{black!60}{Sep 2018 -- Oct 2019}}{Research Software Engineer, Fraunhofer-Center for Maritime Logistics CML}{Hamburg, Germany}
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\item Contributed \textbf{ML and NLP} components to ARTUS, a Fraunhofer research project for automatic sea rescue speech transcription that combined speech recognition and machine learning for a safety-critical maritime domain.
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\item Set up Jenkins CI/CD pipeline with quality gates independently, introducing build automation to the research team; developed SCEDAS crew scheduling software (C\#, .NET, MS SQL Server, Entity Framework).
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\item Built microservices (Express.js, \textbf{Docker}, SQLite) for MISSION, a Fraunhofer research platform for maritime data exchange between logistics stakeholders including ports, operators, and research partners.
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\item Contributed to a Fraunhofer CML research grant proposal for ML-based predictive maintenance of maritime equipment, applying time-series analysis and ML to equipment condition data and maintenance timing prediction.
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\end{rSubsection}
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% --- Vizrt (Jul 2017 -- May 2018) — 2 bullets: VZ-1, VZ-2 ---
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\begin{rSubsection}{Broadcast Video Data Processing \& Python/C++ Backend Engineering}{\textcolor{black!60}{Jul 2017 -- May 2018}}{DevOps Engineer, Vizrt}{Bergen, Norway}
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\item Engineered distributed video transcoding backend components in \textbf{Python} and C++ for Vizrt's broadcast platform, processing A/V data at scale for global media customers including CNN, BBC, and Al Jazeera.
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\item Built automated integration and unit test suite for A/V streaming (\textbf{Python}) and integrated quality gates into CI/CD, which shortened the feedback loop for new features and raised overall release quality.
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\end{rSubsection}
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% --- Generali (May 2015 -- Jun 2017) — 2 bullets: GN-1, GN-3 ---
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\begin{rSubsection}{Test Automation \& BDD Technical Ownership}{\textcolor{black!60}{May 2015 -- Jun 2017}}{IT Consultant, Generali Deutschland Informatik Services}{Hamburg, Germany}
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\item Introduced BDD test automation to Generali (Serenity-BDD, Selenium, JBehave), running the initial PoC and taking technical ownership; trained project teams and presented the methodology across the Java Community.
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\item Developed UIPath RPA proofs of concept at Generali GDIS and served as internal RPA contact for Generali group companies --- extending automation from test tooling into business process automation.
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\item Developed Java/J2EE application features for the PIA-Postkorb workflow portal; migrated WebServices to the XLDeploy process and contributed to an Apache Camel / Spring Boot dispatcher integration PoC.
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\end{rSubsection}
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% EDUCATION — FIXED
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Education}
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{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Oct 2010 -- Jul 2013}}\\
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{Universit\"at der Bundeswehr M\"unchen}; thesis at Tongji University, Shanghai \hfill Thesis Grade: \textbf{1.0}\\
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{\small Thesis: \textit{Development of a Web-Based Remote Fault Diagnosis System} (Neural Networks, PSO, Fuzzy Logic)}
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{B.Eng.\ Information and Telecommunication Technologies} \hfill {\textcolor{black!60}{Oct 2007 -- Sep 2010}}\\
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{Universit\"at der Bundeswehr M\"unchen}, Munich, Germany
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% CERTIFICATIONS & AWARDS — FIXED
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%----------------------------------------------------------------------------------------
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\begin{rSection2}{Certifications \& Awards}
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\item \textbf{IBM AI Engineering Specialization}, Coursera. Deep learning, TensorFlow, Keras, Apache Spark ML.
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\item \textbf{AI for Trading Nanodegree}, Udacity / WorldQuant (2021). Quantitative ML, time-series analysis.
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\item \textbf{AWS Certified Solutions Architect -- Associate}, Amazon Web Services (2024, active until Sep 2027).
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\item \textbf{Data Engineering with AWS Nanodegree}, Udacity (2026). AWS data pipeline architecture.
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\item \textbf{iSAQB CPSA -- Foundation Level}, iSAQB (2016). Certified Professional for Software Architecture.
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\item \textbf{ITIL Foundation Certificate in IT Service Management}, PEOPLECERT / AXELOS (2016).
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\end{rSection2}
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\begin{center}
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\vspace{0.1cm}
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\textit{Languages: German (native), English (fluent)}
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\end{center}
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\end{document}
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