Data Engineer

URUS Group

$95K — $115K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Information Systems, or a related field.
  • 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.
  • Familiarity with Microsoft Azure; knowledge of Microsoft Fabric is a plus.
  • Experience using AI-assisted development tools in engineering workflows.
  • Strong analytical, problem-solving, and collaboration skills.

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 best practices in data warehousing across 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 and documentation.

Benefits

  • Work in a global environment with competitive projects.
  • Opportunity to enhance skills in modern data technologies and AI.
  • Collaborative culture promoting continuous learning and improvement.
  • Support for professional development and emerging practices in data engineering.
Full Job Description
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.


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