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Data Engineer
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Robert Half
Englewood, CO (In Person)
Full-Time
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Job Description
Description We are looking for an experienced Data Engineer to join a long-term contract engagement in Colorado. This position centers on designing and improving cloud-based data solutions that support enterprise analytics and AI-driven initiatives. The role is ideal for a hands-on, detail-oriented individual who excels in preparing reliable, scalable data assets and partnering with technical teams in a modern Microsoft-focused environment.
Responsibilities:
- Design, develop, and enhance scalable data pipelines to support enterprise reporting, analytics, and AI use cases.
- Transform, cleanse, and organize complex data from multiple sources so it can be used effectively by downstream engineering and machine learning teams.
- Build and maintain robust data workflows in a cloud-based ecosystem using Microsoft Fabric, Synapse Analytics, Azure Data Factory, and Databricks.
- Collaborate with data engineers, ML engineers, and other technical stakeholders to create efficient and dependable data architecture.
- Monitor pipeline performance and resolve issues related to data quality, reliability, and processing efficiency across platforms.
- Orchestrate data ingestion, movement, and transformation processes for large-volume datasets in a backend-focused engineering environment.
- Support AI and machine learning initiatives by delivering well-structured datasets optimized for model development and operational use.
- Contribute to scalable lakehouse or warehouse-oriented data solutions while helping align engineering practices with cloud data best practices. Requirements
- 7+ years of experience in data engineering or a closely related technical field.
- Strong programming ability in Python and solid experience developing enterprise-grade ETL and data integration solutions.
- Hands-on expertise with Microsoft Fabric and practical experience working with Synapse Analytics and Azure Data Factory.
- Proven experience using Databricks to build, manage, and optimize large-scale data processing solutions.
- Strong understanding of cloud-based data engineering principles, including pipeline design, orchestration, and performance tuning.
- Experience working with large datasets and distributed processing technologies such as Apache Spark or PySpark.
- Familiarity with modern data platforms and architectures, including lakehouse, data warehouse, or hybrid cloud data environments.
- Working knowledge of tools and technologies such as Microsoft SQL, Apache Kafka, or Hadoop is beneficial.