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Senior Data Engineer (Databricks & Cloud Analytics)
About Us
STAFFXPERT LLC is a trusted staffing and workforce solutions provider connecting top talent with leading organizations across technology, engineering, healthcare, finance, and professional services. We are committed to helping skilled professionals find meaningful opportunities where they can make a lasting impact and advance their careers.
Job Title
Senior Data Engineer (Databricks & Cloud Analytics)
Location
Salt Lake City / Midvale, Utah (Onsite)
Job Summary
STAFFXPERT LLC is seeking a Senior Data Engineer (Databricks & Cloud Analytics) on behalf of our client in Salt Lake City/Midvale, UT. This role is ideal for a highly skilled data engineering professional with expertise in Databricks, Apache Spark, and cloud-based analytics platforms. The successful candidate will be responsible for designing, building, and optimizing scalable data solutions that support enterprise reporting, analytics, and data-driven decision-making. This position offers the opportunity to work with modern data technologies in a collaborative and fast-paced environment.
Key Responsibilities
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Design, develop, deploy, and maintain scalable enterprise data engineering solutions.
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Build and optimize ETL/ELT pipelines using Databricks, Apache Spark (PySpark), Delta Lake, and Azure Data Factory.
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Develop high-performance data ingestion, integration, and transformation frameworks.
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Create, optimize, and maintain complex SQL queries, stored procedures, and data processing workflows.
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Design scalable data models to support business intelligence, analytics, and AI initiatives.
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Develop and integrate solutions using REST APIs, JSON, XML, and SOAP-based services.
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Implement and support real-time data streaming and messaging solutions using Apache Kafka.
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Monitor, troubleshoot, and improve production data pipelines to ensure reliability and performance.
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Apply data governance, security, data quality, and compliance best practices.
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Collaborate with architects, product owners, business analysts, and engineering teams to deliver technical solutions aligned with business objectives.
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Participate in Agile development processes, including planning, refinement, code reviews, and retrospectives.
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Create technical documentation, deployment guides, and operational support materials.
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Mentor junior engineers and contribute to technical standards and best practices.
Required Qualifications
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Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent professional experience.
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6+ years of experience designing, developing, and supporting enterprise data platforms or cloud-based analytics solutions.
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Strong hands-on experience with:
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Databricks
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Apache Spark (PySpark)
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Python
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SQL
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Scala
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Experience working with Databricks technologies, including:
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Delta Lake
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Unity Catalog
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Delta Live Tables (DLT)
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Databricks SQL
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MLflow
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Databricks Jobs
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Strong SQL development, query optimization, and performance tuning expertise.
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Experience building ETL/ELT solutions using Azure Data Factory or similar cloud integration platforms.
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Experience with Apache Kafka or other event-streaming technologies.
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Experience integrating enterprise systems using REST APIs, SOAP services, JSON, and XML.
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Experience with cloud-based data storage platforms such as Azure Data Lake Storage (ADLS Gen2).
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Proficiency with Git and collaborative software development practices.
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Experience working in Linux environments and using shell scripting.
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Strong analytical, troubleshooting, and problem-solving skills.
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Experience working within Agile methodologies such as Scrum, Kanban, or SAFe.
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Excellent communication and collaboration skills with both technical and non-technical stakeholders.
Preferred Qualifications
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Experience with Microsoft Azure cloud services.
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Experience with Azure Synapse Analytics and Microsoft Fabric.
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Knowledge of Power BI and enterprise reporting solutions.
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Familiarity with Infrastructure as Code (IaC) tools such as Terraform.
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Experience implementing CI/CD pipelines using Azure DevOps, GitHub Actions, or similar platforms.
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Understanding of DataOps, MLOps, data governance, and metadata management practices.
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Experience with enterprise data warehousing and Master Data Management (MDM).
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Experience supporting financial services, public sector, or other highly regulated data environments.
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