Senior Azure Data Engineer

Eliassen Group; Columbus (OH); Remote; USD 103,000 - 117,000; Full time

Job summary

Eliassen Group is seeking a Senior Azure Data Engineer to modernize an enterprise data platform by migrating data from on-premises Oracle and IBM DataStage to a cloud-based Azure data lake. You will design, build, and optimize data pipelines with Azure Databricks and Data Factory, guiding architecture decisions and engineering best practices.

You will ingest data from legacy sources, implement data-quality checks, and promote data through Bronze, Silver, and Gold layers while collaborating with

Qualifications

• Strong hands-on data engineering experience in enterprise environments.
• Experience with Azure Databricks and Azure Data Factory.
• Strong SQL and Python or PySpark development skills.
• Experience designing and developing batch data pipelines and ETL/ELT workflows.
• Experience implementing data-quality, data-validation, and reconciliation processes.
• Knowledge of medallion or lakehouse architecture (Bronze, Silver, Gold).
• Experience with relational databases and large datasets.
• Understanding of data modeling, partitioning, and performance optimization.
• Experience with Git or another version-control platform.
• Experience with CI/CD and automated deployment practices.

Responsibilities

• Design, develop, test, and maintain data pipelines using Azure Databricks and Azure Data Factory.
• Support migration of data and ETL workloads from on-premises Oracle/DataStage to Azure cloud data lake.
• Develop scalable ingestion, transformation, and data-processing solutions.
• Implement medallion architecture across Bronze, Silver, Gold layers.
• Incorporate data-quality and validation checks into pipelines and workflows.
• Automate validation and testing to improve data product reliability.
• Guide technical discussions on data architecture and design standards.
• Troubleshoot pipeline failures and data-quality issues.
• Collaborate with engineers, analysts, architects, and stakeholders to translate requirements into data solutions.
• Participate in code reviews, documentation, testing, deployment, and continuous-improvement activities.
• Promote reusable development patterns and sound engineering practices.

Skills

Azure Databricks

Azure Data Factory

SQL

Python

PySpark

Git

CI/CD

Azure DevOps

Tools

DataStage

Job description

Senior Azure Data Engineer

Anywhere

Type: Contract-to-Hire

Category: Development

Industry: Insurance

Workplace Type: Remote

Standard Hours: Open

Description:

Hybrid Bloomington MN

As a Senior Azure Data Engineer, you will help modernize an enterprise data platform by migrating data and workloads from an on-premises Oracle data warehouse and IBM 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. This role requires someone who can contribute directly to development while also helping guide data architecture decisions, technical design discussions, and engineering best practices. Your work will include ingesting data from legacy and enterprise source systems, implementing data-quality and validation processes, and promoting data through medallion architecture layers. You will collaborate with engineers, architects, analysts, and business partners to build reliable, maintainable, and trusted data products. Healthcare or health-plan experience is beneficial, particularly experience with claims, billing, finance, or plan-administration data, but strong Azure Databricks and data-platform engineering capabilities are the primary priorities.

Rate: $75.00 to $85.00/hr. w2 Contract to hire available

Responsibilities:

• Design, develop, test, and maintain data pipelines using Azure Databricks and Azure Data Factory.
• Support the migration of data and ETL workloads from an on-premises Oracle and DataStage environment to an Azure cloud data lake.
• Develop scalable ingestion, transformation, and data-processing solutions.
• Design and implement medallion architecture patterns across Bronze, Silver, and Gold data layers.
• Build data-quality, reconciliation, and validation checks directly into data pipelines and engineering workflows.
• Automate validation and testing to improve the reliability of data products.
• Help guide technical discussions and decisions related to data architecture, solution design, and engineering standards.
• Troubleshoot pipeline failures, data-quality issues, and performance problems.
• Collaborate with engineers, analysts, architects, and business stakeholders to translate requirements into practical data solutions.
• Participate in code reviews, documentation, testing, deployment, and continuous-improvement activities.
• Promote reusable development patterns and sound engineering practices across the team.

Experience Requirements:

• Strong hands-on data engineering experience in enterprise environments.
• Demonstrated experience with Azure Databricks and Azure Data Factory.
• Strong SQL and Python or PySpark development skills.
• Experience designing and developing batch data pipelines and ETL or ELT workflows.
• Experience implementing data-quality, data-validation, and reconciliation processes.
• Knowledge of medallion or lakehouse architecture, including Bronze, Silver, and Gold data layers.
• Experience working with relational databases and large, complex datasets.
• Understanding of data modeling, schema design, partitioning, and performance optimization.
• Experience with Git or another version-control platform.
• Experience working with CI/CD and automated deployment practices.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to contribute directly to development while providing technical guidance to other team members.
• Strong communication and collaboration skills.
• Preferred: experience migrating on-premises platforms to Azure, Oracle and IBM DataStage, Azure data lake or lakehouse design, Delta Lake and Databricks features, automated data testing and monitoring, Azure DevOps, Agile or Scrum, healthcare or finance-related data, and regulated enterprise environments.