Data Engineer
Title: Data Engineer Location: Greenwood Village, CO Pay: $60-$68 per hour Duration: 1 year Contract to hire Hybrid: 3 days onsite and 2 remote Job DescriptionA client of Insight Global is seeking a Senior Data Engineer to support a high-impact Connectivity project, focused on building and maintaining scalable data pipelines that ingest, clean, transform, and integrate both structured and real-time streaming data. This role partners closely with Data Scientists to deliver production-ready datasets that power advanced analytics and machine learning models.Key ResponsibilitiesDesign, build, and maintain robust data pipelines for both batch and real-time data ingestion, including structured sources and raw streaming data from KafkaClean, transform, and standardize incoming data to ensure high-quality, analytics-ready datasetsIntegrate curated data pipelines with machine learning models developed by Data Science teamsDevelop and optimize workflows using Apache Airflow for scheduling and orchestrationBuild cloud-native data solutions leveraging AWS, including S3, Kubernetes, and related servicesWrite high-quality, scalable code in Python and ScalaSupport deployment, monitoring, and performance tuning of data pipelines in production environmentsCollaborate cross-functionally with Data Scientists, Software Engineers, and Product teams to align data solutions with business needsRequired Skills & Experience5–7+ years of experience as a Data Engineer or in a similar roleStrong hands-on experience with Python and ScalaExperience working with AWS cloud services, specifically S3Expertise in Kubernetes for containerized workloadsExperience with Apache Airflow for workflow orchestrationHands-on experience processing real-time streaming data (Kafka or similar)Nice to Have Skills & ExperienceMaster’s degree in Computer Science, Data Engineering, or a related fieldPrior experience supporting data pipelines for machine learning or advanced analytics use casesExperience working on connectivity, network, or platform-oriented data projectsBackground in productionizing data solutions in complex enterprise environments