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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Dahl Consulting - **Location:** Brooklyn Park, MN, United States - **Experience:** Expert - **Salary:** $140,733.0 - $234,582.0 - **Contract:** Temporary to permanent - **Skills:** Artificial Intelligence, Airflow, Automation of Tests, Big Data, BigQuery, Cloud Storage, Code Review, Continuous Integration, Data as a Services, Data Validation, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Distributed Computing Environment, Data Flow Control, Fraud Prevention and Detection, Apache Hadoop, Hadoop Distributed File System, Apache Hive, Python (Programming Language), Machine Learning, Apache Oozie, Operational Databases, Cloudera, Scaled Agile Framework, Software Deployment, Software Engineering, SQL Databases, Data Streaming, Workflow Management Systems, Enterprise Data Management, Data Logging, Google Cloud, Apache Spark, Git, Apache Flink, Apache Kafka, Spark Streaming, Machine Learning Operations, Video Streaming, Stream Processing, Data Pipelines, Serverless Computing - **Published:** October 8, 2026 - **Apply:** https://www.dice.com/job-detail/cc5cac8c-336e-4983-b47e-4932e051984a ## About the Role * 5+ years of hands-on data engineering or related software engineering experience building large-scale data systems. * Strong proficiency in Python and SQL. * Hands-on experience with Apache Spark and distributed data processing. * Experience with the Hadoop ecosystem, including HDFS and Hive. * Hands-on experience with Kafka or another event-streaming technology. * Experience building and supporting ETL/ELT and data integration pipelines. * Experience with Google Cloud Platform data services, particularly BigQuery. * Experience optimizing data pipelines for performance, scalability, reliability, and cost. * Experience supporting production data systems, including monitoring, logging, incident troubleshooting, and operational support. * Working knowledge of Git, CI/CD, automated testing, and Agile engineering practices. * Ability to independently own data engineering work from design through production deployment and ongoing support. * Strong communication and collaboration skills with the ability to work effectively across technical and business teams., * Experience with additional Google Cloud Platform services such as Dataproc, Dataflow, Pub/Sub, or Cloud Storage. * Experience migrating Hadoop workloads or enterprise data platforms to Google Cloud Platform or another public cloud environment. * Experience with Spark Streaming, Flink, or similar stream-processing frameworks. * Experience with data governance, lineage, security, privacy, and data quality frameworks. * Experience with orchestration tools such as Airflow, Cloud Composer, Oozie, or similar technologies. * Experience supporting machine learning data pipelines or Google Cloud Platform Vertex AI/MLOps workflows. * Experience working with high-volume enterprise data in fraud, risk, security, retail, or e-commerce environments. ## Description We are hiring a Senior Data Engineer on behalf of our client, a large national retailer with a sophisticated technology organization supporting high-volume digital and enterprise operations. This is a hands-on, execution-focused opportunity supporting a Data Services Engineering team within a fraud and abuse technology environment. The Senior Data Engineer will build, modernize, and operate large-scale data pipelines and platforms across Hadoop and Google Cloud Platform (Google Cloud Platform). The ideal candidate is a strong individual contributor who can quickly become productive within an established architecture, independently own data engineering work from design through production support, and deliver reliable solutions for high-volume batch and streaming workloads. This is a 6-12 month contract position with potential for extension. The position follows a hybrid schedule with two days per week onsite, * Design, build, test, deploy, and support scalable batch and streaming data pipelines using Python, SQL, Apache Spark, Kafka, and related technologies. * Support the modernization and migration of data pipelines and datasets from on-premises Hadoop environments to Google Cloud Platform (Google Cloud Platform), including BigQuery and other cloud-native services. * Build and optimize ETL/ELT workflows for large-scale, high-volume datasets with an emphasis on performance, scalability, reliability, and cost efficiency. * Develop trusted datasets supporting analytics, fraud investigation, reporting, and machine learning use cases. * Implement appropriate data quality checks, testing, and validation processes. * Troubleshoot production data issues, conduct root-cause analysis, and implement durable solutions to prevent recurring failures. * Implement and maintain monitoring, alerting, automation, and CI/CD practices for production data pipelines. * Work across both legacy Hadoop and modern Google Cloud Platform environments during ongoing platform modernization. * Partner with engineers, analysts, data scientists, product teams, and other stakeholders to understand data requirements and deliver effective solutions. * Identify technical risks, data quality concerns, performance issues, and opportunities to improve platform reliability. * Contribute to technical design, code reviews, engineering standards, documentation, and knowledge transfer.