10/27/2026 updated
100 % available
Data Engineer | Python | SQL | Cloud-Native Data Platforms | ETL/ELT Pipelines
Shiedam, Netherlands
Only remote
Senior Data EngineerAbout me
I’m a Data Engineer focused on building reliable data pipelines that turn raw data into something businesses can actually use. I’ve worked with SaaS, mobile apps, e-commerce, and data-heavy platforms, covering ingestion, transformation, orchestration, data quality, and analytics.
Azure DatabricksApache AirflowData AnalysisPython (Programming Language)PostgreSQLDocker ContainerSnowflakeApache SparkFastAPIKubernetesApache KafkaTerraform
âš¡ Building and running data platforms across healthcare, fintech and real estate projects: a HIPAA-aligned lakehouse, real-time streaming and analytics warehouses used daily by analytics, product and ML teams.
Designed Kafka pipelines ingesting ~5M events a day with under 1 minute end-to-end latency for analytics and ML.
Built Databricks + Airflow workflows for batch and ML feature engineering; automated retries cut manual reruns by ~60%.
Developed bronze/silver/gold Delta Lake models covering 50+ tables for reporting and model training.
Put HIPAA-aligned controls in place: encryption at rest and in transit, audit logging and RBAC across all patient-data layers.
Built FastAPI microservices for governance and access control, removing ~15 hours a week of manual requests.
Shipped dbt, Fivetran and Python ELT across PostgreSQL and Redshift, integrating 10+ internal and third-party sources.
Tuned Redshift (dist/sort keys, incremental models): dashboard queries dropped from ~30s to under 5s, storage costs ~25% lower.
Ran event-driven AWS setups (Lambda, Kinesis, S3), moving product analytics from next-day batch to near real-time.
Built Airflow ingestion into Redshift and BigQuery with 99%+ on-time daily delivery.
Added Great Expectations/Soda checks, Grafana dashboards and DataHub lineage, cutting incident detection from hours to minutes and data-quality incidents by ~40%.
Stack: Python, SQL, Kafka, Databricks, Spark, Airflow, dbt, Fivetran, AWS, Redshift, BigQuery, PostgreSQL, FastAPI, Docker, Kubernetes, Terraform, Great Expectations, Soda, DataHub, Grafana, Looker.
Experience
Professional experience0 - 5 years
Freelancer since2022
Languages
EnglishFluent
Project history
Built and optimized internal data tools and Python microservices for platform automation, data governance, and access control. Designed Kafka-powered streaming pipelines for real-time ingestion of user activity logs. Implemented Databricks and Airflow-based workflows for batch processing and ML feature engineering. Developed and maintained data models in the lakehouse (bronze/silver/gold layers). Supported HIPAA-aligned data flows with encryption, audit logging, and role-based access control. Introduced data quality checks and alerting, built observability dashboards in Grafana, and documented system lineage in DataHub. Tech Stack: Python, SQL, Apache Kafka, Databricks (PySpark, Delta Lake), Apache Airflow, AWS (S3, Lambda, IAM, KMS, CloudWatch), PostgreSQL, FastAPI, Docker, Kubernetes, Terraform, Grafana, Prometheus, DataHub, Great Expectations, Pytest, Git, GitHub Actions, Jira, Confluence.
Developed production-grade ETL/ELT pipelines using dbt, Fivetran, and custom Python scripts across PostgreSQL and Redshift. Designed and maintained dbt models including staging, intermediate, and mart layers. Managed cloud-native infrastructure using AWS Lambda, S3, Kinesis, and IAM for real-time data streaming. Created event-driven architectures for operational analytics and product personalization. Optimized Redshift query performance and contributed to a platform-wide migration toward secure microservices architecture. Supported data governance with dbt documentation, Soda quality checks, and Looker dashboards. Tech Stack: Python, SQL, dbt, Fivetran, PostgreSQL, Amazon Redshift, AWS (Lambda, S3, Kinesis, IAM, CloudWatch), Soda, Looker, Docker, Terraform, Git, GitHub Actions, Jira, Confluence.
Built batch ingestion pipelines using Python and Airflow to load data from internal databases, APIs, and flat files into Redshift and BigQuery. Developed and scheduled Airflow DAGs with dependency management, retries, and failure alerts. Maintained ETL workflows with emphasis on data quality, schema validation, and compliance. Wrote and optimized SQL transformations and assisted in modeling centralized data warehouse schemas for finance, sales, and customer analytics. Documented data sources, pipeline logic, and table definitions. Tech Stack: Python, SQL, Apache Airflow, Amazon Redshift, Google BigQuery, PostgreSQL, AWS S3, Pandas, Git, GitHub, Jira, Confluence.