Location: Netherlands, The Hague, CET
Remote: Yes. Hybrid in The Hague/Amsterdam/Randstad also works.
Willing to relocate: Open within the EU+EFTA for the right role. US/UK only with serious relocation support.
Technologies:
Core: Python, SQL, R
Data engineering: PySpark/Spark, Databricks, dbt, Airflow/Dagster/Prefect, Kafka, Polars
Warehouses/lakes: BigQuery, Snowflake, PostgreSQL, DuckDB
Cloud/infra: Azure, GCP, AWS, Docker, Kubernetes fundamentals, Terraform
Data quality: Great Expectations, Pandera
ML/NLP: XGBoost, Keras, PyTorch, SetFit
Also some Go, Rust, Scala
Résumé/CV: https://drive.google.com/file/d/1qRsq9QoQF0hZ2FSmhaidGTUd1JmHSb-M/view?usp=sharing
LinkedIn: https://www.linkedin.com/in/v-vilimaitis/
Email: vladimirvilimaitis _at_ gmail _dot_ com
Senior data/platform engineer, around 5 years of experience.
I build data systems where the output has to be trusted. Recent work includes validation frameworks for EU regulatory reporting at ESMA, Spark/Databricks pipelines for logistics use cases for SHV Energy, ESG reporting for KPMG, healthcare migration/data quality work around openEHR, and BigQuery/Airflow pipelines for logistics analytics.
I’m strongest in messy-source-data problems: ETL/ELT, data quality, warehouse/lakehouse modeling, pipeline reliability, cloud data platforms, and making analytical systems debuggable by people who were not there when they were built.
Looking for: data engineering, data platform, backend/data infrastructure, analytics engineering with real engineering depth, machine learning/data-heavy DS roles, ML/LLM infrastructure, or small technical teams building data products.
I build data systems where the output has to be trusted. Recent work includes validation frameworks for EU regulatory reporting at ESMA, Spark/Databricks pipelines for logistics use cases for SHV Energy, ESG reporting for KPMG, healthcare migration/data quality work around openEHR, and BigQuery/Airflow pipelines for logistics analytics.
I’m strongest in messy-source-data problems: ETL/ELT, data quality, warehouse/lakehouse modeling, pipeline reliability, cloud data platforms, and making analytical systems debuggable by people who were not there when they were built.
Looking for: data engineering, data platform, backend/data infrastructure, analytics engineering with real engineering depth, machine learning/data-heavy DS roles, ML/LLM infrastructure, or small technical teams building data products.
Available immediately.