\documentclass[11pt,a4paper,roman]{moderncv}
\usepackage[english]{babel}
\moderncvstyle{classic}
\moderncvcolor{green}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{ragged2e}
\usepackage[scale=0.80]{geometry}
\usepackage[version=4,arrows=pgf-filled]{mhchem}
\renewcommand*{\makeletterclosing}{\par\vspace{2ex}\closingname\par}
\microtypesetup{expansion=false}

% ========== HEADER ==========
\name{Dennis}{Thiessen, M.Eng.}
\address{Bern, Switzerland}{}{}
\phone[mobile]{+41~795~955~585}
\email{dennis@thiessen.io}
\extrainfo{\href{https://linkedin.com/in/dennis-thiessen}{linkedin.com/in/dennis-thiessen}}
% ============================

\begin{document}

\recipient{Hiring Team}{Merchant Data Science, Merchant Shopping\\Google Switzerland GmbH\\Z\"urich, Switzerland}
\date{\today}
\opening{Dear Merchant Data Science Hiring Team,}
\makelettertitle

\begin{justify}

Google's Shopping Graph now indexes over 60 billion product listings, and the Merchant Data Science team in Z\"urich builds the data foundation that turns that scale into decisions merchants and shoppers act on. That is the work I do today, one scale down. At Swisscom, Switzerland's largest telco, I design and own self-service, governed data products that analytics and engineering teams depend on. The role's explicit aim, bridging software engineering, data engineering, and data science, is how I already work as a Component Owner. Based in Bern, I'd be local to the Z\"urich team.

Most of my last decade has been pipeline design and dimensional modeling in production. As Swisscom moved to a cloud-native AWS platform (Glue, Athena/Iceberg, Redshift, Airflow), I migrated my Fulfillment and Product Analysis ETL off Teradata and Oracle, modeled the dimensional schemas behind it, and own the business-critical Fulfillment pipelines that feed it under an on-call SLA. On that platform I build the self-serve data products other teams query without coming back to me, the durable, reusable assets your posting describes. For heavy workloads I use PySpark across our Data Lake; that distributed-processing experience carries over to Google's Flume and Dataflow stack, though not by name.

Two things round out the fit. Data quality and reliability are not afterthoughts for me: at Bosch I built the anomaly-detection and monitoring stack (ELK, Grafana, Prometheus) for 24/7 manufacturing data and moved production ML inference into a 300mm wafer fab, where there are no maintenance windows. I also care about how people consume data. I co-owned Bosch's TIBCO Spotfire analytics platform and spoke at the TIBCO Analytics Forum 2022 on visualizing spatial variability in semiconductor products. Building data products that AI-powered Shopping experiences and human analysts rely on is where I want to work next, and I'd welcome a conversation about the team's roadmap.

\end{justify}

\vspace{0.3cm}
{Sincerely,\\
Dennis Thiessen, M.Eng.\\
Staff Data, Analytics \& AI Engineer\\
Swisscom (Schweiz) AG}

\end{document}
