Job description

VAS, a URUS Group company, is looking for a Data Engineer to help design, build, and improve the data solutions that support our global business. Working primarily in Databricks, you’ll develop and maintain pipelines and datasets that power reporting, analytics, and AI/ML workloads. This role works across both modern and established data technologies, with a focus on improving performance, reliability, data quality, and scalability. We’re looking for someone with strong data engineering fundamentals, hands-on Databricks experience, and a solid understanding of data warehousing and Microsoft-based data environments. Responsibilities • Design, build, and maintain scalable ETL/ELT pipelines and datasets in Databricks using Python, Spark, and SQL. • Develop and support batch and streaming data architectures for reporting, analytics, and AI/ML workloads. • Troubleshoot and optimize Databricks and existing data integration processes for performance, reliability, and data latency. • Support and enhance existing SQL Server, SSIS, and SSRS solutions while contributing to ongoing platform modernization. • Implement data quality checks, validation, cleansing, and data warehousing best practices across data pipelines and reporting environments. • Integrate Databricks with enterprise applications, databases, and other data sources. • Partner with business users, analysts, and IT teams to translate reporting and data needs into scalable technical solutions. • Contribute to continuous improvement through emerging data engineering practices, AI-assisted development tools, documentation, and ongoing learning Requirements • Bachelor’s degree in Computer Science, Information Systems, or a related field. • Strong hands-on experience with Databricks, including Python, Spark, and Delta Lake. • Strong SQL Server and data warehousing experience, including T-SQL. • Experience with SSIS and SSRS. • Experience with Microsoft Azure; Microsoft Fabric is a plus. • Experience using AI-assisted development tools as part of the engineering workflow. • Strong analytical, problem-solving, communication, and collaboration skills.

Requirements

Must-haves

Design, build, and maintain scalable ETL/ELT pipelines and datasets in Databricks using Python, Spark, and SQL.
Develop and support batch and streaming data architectures for reporting, analytics, and AI/ML workloads.
Troubleshoot and optimize Databricks and existing data integration processes for performance, reliability, and data latency.
Support and enhance existing SQL Server, SSIS, and SSRS solutions while contributing to ongoing platform modernization.
Implement data quality checks, validation, cleansing, and data warehousing best practices across data pipelines and reporting environments.
Integrate Databricks with enterprise applications, databases, and other data sources.
Partner with business users, analysts, and IT teams to translate reporting and data needs into scalable technical solutions.
Contribute to continuous improvement through emerging data engineering practices, AI-assisted development tools, documentation, and ongoing learning
Bachelor’s degree in Computer Science, Information Systems, or a related field.
Strong hands-on experience with Databricks, including Python, Spark, and Delta Lake.
Strong SQL Server and data warehousing experience, including T-SQL.
Experience with SSIS and SSRS.
Experience using AI-assisted development tools as part of the engineering workflow.
Strong analytical, problem-solving, communication, and collaboration skills.

Nice-to-haves

Experience with Microsoft Azure; Microsoft Fabric is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpythonsqlazure

Platform: oracle-cloud
Effort: medium