ASML
Senior Data Engineer
Feb 2024 to Present
Python
Terraform
Event Hubs
Data Explorer
Event Grid
DataBricks
Experience
Royal Flora Holland
Senior Data Engineer
May 2023 to Feb 2024
Python
Airflow
Kafka
CDK
Spark
DataBricks
Docker
Postgres
ECS
SQS
SNS
Lambda
Implementation and maintenance of scalable data pipelines to support actively running machine learning models in production.
Renewi
Solution Architect
Nov 2021 to Mar 2022
Terraform
dbt
Machine Learning
ADF
Design and rollout of the Enterprise Data Lake. Support migration of on-prem pipelines to cloud.
Rabobank
Solution Architect / Senior Data Engineer
Mar 2020 to Oct 2021
Python
Scala
Airflow
Devops
ADF
DataBricks
Design of the Enterprise Data Lake (EDL). Design of EDL onboarding automation onto Azure. Design and implementation of EDL ingestion automation framework using ADF, Databrick and Python.
Royal Schiphol Group
Senior Data Engineer
Jun 2018 to Mar 2020
Python
Scala
Kafka
Docker
Kubernetes
Event Hubs
Designing and implementing parts of a Big Data platform. Leverage streaming data for analytics and machine learning. Member of the Deep Turnaround team to process and scale streaming 4k images to predict turnaround times.
Unilever
Senior Data Engineer / Solution Architect
Jan 2018 to Jul 2018
Scala
Spark
DataBricks
Airflow
Migration of SQL scripts to scalable Spark jobs. Implementation of a data lake.
Tele2
Lead Data Engineer
Oct 2017 to Apr 2018
Scala
Spark
Airflow
Leading the data engineering team in the implementation of scalable machine learning models.
Quby
Machine Learning Engineer
May 2017 to Oct 2017
Spark
DataBricks
Scala
Airflow
S3
At Quby, Tim worked on the development of the Eneco WasteCheck app, a project from the parent company Eneco. The app aimed to provide insights into energy consumption by disaggregating aggregated energy signals from smart meters, like the Toon, into specific household energy patterns (for example, for appliances like washing machines, dryers, refrigerators, stoves, and showers). When Tim joined the team, a successful Proof of Concept (PoC) had already been completed. His role was to bring this PoC to a production-ready state. This involved migrating the implementation from Pandas to Apache Spark to enhance scalability and efficiency. He also translated sequential algorithms into vectorized operations, significantly improving processing power and speed. The project was completed with the full production implementation of the app, using Scala and Apache Spark. Apache Airflow, installed on EC2 instances, served as the scheduler for batch processing. The processed data was stored daily in S3 in Parquet format, ensuring efficient storage and fast access for further analysis. In addition to his technical contributions, Tim provided Scala training to the team and introduced DevOps best practices, further improving operational efficiency and code quality.
