Job title: Data Engineer
Location: [ Poland remote]
Work mode: [ Remote]
Type of cooperation: [B2B]
Salary range: [B2B: 120–140 net/hour]
About the company/project:
Responsibilities:
Build and maintain multi-layer pipelines (Landing → Raw → Enriched → Curated) on Azure Databricks (Spark, Delta Lake)
Orchestrate pipelines via Databricks Jobs / Workflows: dependencies, retries, alerting
Implement batch and API-based data ingestion from multiple source systems
Apply schema enforcement and handle schema drift; run data quality checks at ingestion
Work hands-on with dbt for pipeline observability and data governance tasks
Monitor job failures, data freshness, and pipeline health against SLA-based delivery targets
Set up alerts, logging, and retry strategies for production pipelines
Work within a controlled Dev/Test/Prod environment: access control, audit trails, pipeline documentation, traceability to requirements
Contribute to CI/CD and MLOps/AI pipelines on Databricks where the project needs it
You are an ideal candidate if you have:
- Strong hands-on experience with Azure Databricks: Spark, Delta Lake
- Build and maintain multi-layer pipelines: Landing → Raw → Enriched → Curated
- Schema enforcement and schema-drift handling
- Data quality checks at ingestion
- Orchestration via Databricks Jobs / Workflows: dependencies, retries, alerts
- Batch and API-based data ingestion
- Strong hands-on experience with dbt
- Monitor job failures, data freshness, and pipeline health
- SLA-based data delivery
- Alerts, logging, and retry strategies
- Access control models
- Audit trails, lineage, and traceability
- Logging and evidence generation
- Segregation of duties
- Data contracts
- Pipeline documentation
- Controlled environments (Dev/Test/Prod)
- Traceability to requirements
Nice to have:
- CI/CD & DevOps for Data — Git-based development (branching), CI/CD pipelines for data workflows, promoting code across environments, integrating testing into deployment pipelines
- AI/MLOps — MLOps / AI pipelines on Databricks
- Exposure to regulatory domains (GxP, clinical trials, pharmacovigilance)
- Strong SQL engineering / modular, reusable dbt models, tests, and documentation at Analytics Engineer depthDimensional modeling: star
- schema, OBT, curated marts
- Tabular Editor (metadata-as-code concepts)
- Power BI dataset structure, performance, and KPI/semantic consistency
- Experience in the pharmaceutical or biotechnology industry
What we offer:
