Overview
Remote
Depends on Experience
Contract - Independent
Contract - W2
Skills
ADF
ARM
Amazon Kinesis
Amazon Redshift
Amazon S3
Amazon Web Services
Analytics
Apache Kafka
Apache Spark
Cloud Computing
Cloud Security
Collaboration
Communication
Continuous Delivery
Continuous Integration
Data Engineering
Data Lake
Data Modeling
Data Processing
Data Quality
Data Validation
Data Warehouse
Databricks
DevOps
Documentation
ELT
Extract
Transform
Load
Git
GitHub
GitLab
Good Clinical Practice
Google Cloud
Google Cloud Platform
Management
Microsoft Azure
Optimization
Orchestration
PySpark
Python
RBAC
SQL
Snow Flake Schema
Job Details
Databricks Developer
Location: Remote (Amsterdam, Netherlands)
Duration: Long Term
Required Skills
- 2 4 years hands-on experience with Databricks, including Notebooks, Jobs, and Workflows.
- Strong proficiency in PySpark, Spark SQL, and building distributed data processing pipelines.
- Practical experience with Delta Lake (ACID, MERGE, schema evolution, OPTIMIZE/VACUUM).
- 6 7 years of experience in data engineering, ETL/ELT pipeline development, and cloud-based data processing.
- Strong coding skills in Python and SQL.
- Experience with at least one major cloud platform (Azure preferred, AWS/Google Cloud Platform acceptable).
- Hands-on experience with Azure Data Lake, AWS S3, or Google Cloud Storage.
- Familiarity with data modeling concepts: fact/dimension models, star/snowflake schema, and lakehouse architecture.
- Experience using Git and working with version control workflows.
- Experience supporting CI/CD pipelines (Azure DevOps, GitHub Actions, GitLab CI).
- Strong understanding of data validation, schema checks, data quality controls, and error handling.
- Experience with orchestration tools like Databricks Jobs, ADF, Airflow, or Prefect.
- Understanding of cloud security practices: IAM, RBAC, secrets management (Key Vault/Secrets).
- Strong documentation, communication, and cross-team collaboration skills.
Nice-to-Have Skills
- Experience with Unity Catalog, Databricks SQL endpoints, or MLflow.
- Knowledge of streaming technologies: Kafka, Azure Event Hub, AWS Kinesis.
- Exposure to modern cloud data warehouses like Snowflake, Azure Synapse, Redshift, or BigQuery.
- Familiarity with Terraform, ARM templates, or other IaC tools.
- Experience with monitoring and logging tools (CloudWatch, Azure Log Analytics, Datadog).
- Understanding of advanced Spark tuning concepts such as adaptive query execution, autoscaling, and cluster optimization.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.