Remote Azure Data Engineer: Data Lakes & ETL Expert

Eliassen Group; United States; Remote; USD 214,906,000 - 243,560,000; Full time

Job summary

Eliassen Group is seeking a Senior Azure Data Engineer to help modernize an enterprise data platform by migrating from an on-prem Oracle DataStage environment to a cloud-based Azure data lake architecture.

You will design, build, and optimize scalable data pipelines using Azure Databricks and Azure Data Factory, contribute to data-architecture decisions, and promote data quality across medallion layers while collaborating with engineers, analysts, and stakeholders.

Qualifications

• Extensive hands-on data engineering in enterprise environments.
• Proficient with Azure Databricks and Azure Data Factory.
• Strong SQL and Python or PySpark development skills.
• Experience designing batch ETL/ELT pipelines.
• Experience implementing data-quality and reconciliation processes.

Responsibilities

• Design, develop, test, and maintain data pipelines with Azure Databricks and Azure Data Factory.
• Migrate ETL workloads from on-prem Oracle/DataStage to Azure data lake.
• Develop scalable ingestion, transformation, and processing solutions.
• Implement medallion architecture across Bronze, Silver, Gold data layers.
• Build data-quality and validation checks into pipelines and workflows.
• Collaborate with engineers, analysts, architects, and stakeholders to translate requirements.
• Participate in code reviews, testing, deployment, and continuous-improvement activities.

Skills

SQL

Python / PySpark

Data pipeline design

Problem solving

Tools

Azure Databricks

Azure Data Factory

Oracle

IBM DataStage

Job description

Eliassen Group is seeking a Senior Azure Data Engineer to help modernize an enterprise data platform by migrating from an on-prem Oracle DataStage environment to a cloud-based Azure data lake architecture.

You will design, build, and optimize scalable data pipelines using Azure Databricks and Azure Data Factory, contribute to data-architecture decisions, and promote data quality across medallion layers while collaborating with engineers, analysts, and stakeholders.