Location: Salt Lake City, UT
Salary: $60.00 USD Hourly - $65.00 USD Hourly
Description: About the Role We are seeking an experienced Senior Data Engineer to design, develop, and support enterprise-scale data solutions that enable analytics, reporting, and business decision-making. In this role, you will collaborate with solution architects, business analysts, data scientists, and stakeholders to build scalable cloud-based data platforms and high-performance data pipelines using modern data engineering technologies. The position requires expertise in Databricks, Apache Spark, cloud analytics platforms, and enterprise data integration within an Agile development environment.
Responsibilities - Design, develop, test, deploy, and maintain enterprise data engineering solutions using cloud and big data technologies.
- Design and implement scalable ETL/ELT pipelines utilizing Databricks, Apache Spark (PySpark), Delta Lake, and Azure Data Factory.
- Build and maintain high-performance data ingestion, transformation, and integration frameworks for analytics and reporting.
- Develop, optimize, and maintain complex SQL queries, stored procedures, and data transformation processes.
- Design and implement scalable data models supporting business intelligence, analytics, and AI initiatives.
- Build and integrate RESTful APIs, event-driven architectures, and SOAP-based services to support enterprise data exchange.
- Develop and maintain streaming and messaging solutions using Apache Kafka.
- Monitor, troubleshoot, and optimize production data pipelines, perform root cause analysis, and implement long-term solutions.
- Apply data quality, governance, security, and performance best practices across data platforms.
- Manage source code using Git and follow CI/CD and DevOps practices.
- Collaborate with cross-functional teams to translate business requirements into technical solutions.
- Participate in Agile ceremonies including sprint planning, backlog refinement, architecture discussions, code reviews, and retrospectives.
- Create technical documentation, deployment artifacts, testing documentation, and operational runbooks.
- Mentor junior engineers and contribute to engineering standards, best practices, and continuous improvement initiatives.
Qualifications - Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical discipline, or equivalent professional experience.
- 6+ years of experience designing, developing, and supporting enterprise data platforms, cloud analytics solutions, or large-scale data engineering initiatives.
- Strong hands-on experience with:
- Databricks, Apache Spark (PySpark), Python, SQL, Scala
- Experience with the Databricks ecosystem, including:
- Databricks Workspaces, Delta Lake, Unity Catalog, Delta Live Tables (DLT), Databricks SQL, MLflow, Databricks Jobs
- Strong SQL development skills, including query optimization and performance tuning.
- Experience designing and implementing ETL/ELT solutions using Azure Data Factory or similar cloud integration platforms.
- Experience with Apache Kafka or other event streaming technologies.
- Experience integrating enterprise applications using REST APIs, JSON, XML, and SOAP web services.
- Experience with Azure Data Lake Storage (ADLS Gen2) or comparable cloud storage platforms.
- Experience using Git for source code management and collaborative software development.
- Experience working in Linux environments, including shell scripting and command-line utilities.
- Strong analytical, troubleshooting, and problem-solving skills.
- Experience working in Agile environments utilizing Scrum, Kanban, or SAFe methodologies.
- Demonstrated ability to manage multiple priorities while delivering high-quality solutions.
- Proven ability to work independently while mentoring team members and contributing to technical leadership.
- Ability to communicate complex technical concepts to both technical and non-technical audiences.
- Ability to collaborate effectively with cross-functional teams and stakeholders.
Preferred Skills - Infrastructure as Code (IaC) tools such as Terraform.
- CI/CD pipeline implementation using Azure DevOps, GitHub Actions, or similar platforms.
- Microsoft Azure cloud services.
- Azure Synapse Analytics.
- Microsoft Fabric.
- Power BI.
- Data governance and metadata management.
- DataOps and MLOps practices.
- Enterprise data warehousing.
- Master Data Management (MDM).
- Experience working with financial services or public sector data environments.
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