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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer III - **Company:** McDonald's - **Location:** Chicago, IL, United States - **Experience:** Experienced - **Salary:** $138,207.0 - $172,758.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Amazon Web Services, Data Analysis, Business Logic, Microsoft Azure, Big Data, BigQuery, Cloud Storage, Code Review, Computer Programming, Customer Data Management, Information Engineering, Extract Transform Load (ETL), DevOps, Data Flow Control, Python (Programming Language), Node.Js, Scrum Methodology, Cloud Services, Software Deployment, SQL Databases, Data Streaming, Data Processing, Apache Spark, Git, Adobe, Pyspark, Apache Flink, Google Cloud Functions, Tealium, Enterprise Integration, Integration Frameworks, Apache Kafka, Spark Streaming, Stream Processing, Data Pipelines, Apache Beam, User Identification, Confluent - **Published:** August 27, 2026 - **Apply:** https://dejobs.org/x/x/BBCD21DF4D344AA6B71DDAADB3DBE4F6/job/ ## About the Role * 5-8+ years of professional Data Engineering experience. * 3+ years working with cloud-native data platforms on GCP, AWS, or Azure. * Expert-level SQL, including complex joins, window functions, optimization, and large-scale data analysis. * Strong experience designing and developing ETL/ELT pipelines. * Strong kwnoledg of programing languages such as Python, SQL, JavaScript or Node.js * Experience with DevOps & Reliability - CI/CD pipelines, Git-base development workflows. * Experience with streaming and data processing (Kafka, Spark/PySpark, Flink, Dataflow / Apache Beam) Must have: * Experience supporting Customer Data Platforms such as mParticle, Segment, Adobe RTCDP, Braze, Tealium, or similar platforms. * Experience implementing customer attributes, audience segmentation, identity resolution, traits, and activation feeds. * Experience with GCP preferred (BigQuery, Pub/Sub, Dataflow, Cloud Run, Cloud Storage) ## Description We are seeking a highly skilled Data Engineer III to join the mCDP mParticle engineering team. This role is responsible for designing, developing, and maintaining customer data pipelines, audience-building capabilities, activation feeds, and platform integrations that power customer engagement experiences across digital and restaurant channels.The ideal candidate is a hands-on engineer with strong data engineering and analytics experience who can independently own initiatives from requirements through production deployment. This engineer will partner closely with Product, MarTech, Analytics, and Engineering teams to translate complex business requirements into scalable technical solutions while ensuring reliability, quality, and maintainability across the customer data ecosystem.This role requires strong technical execution, problem-solving, and the ability to navigate ambiguity while delivering high-quality solutions with minimal oversight. Duties Customer Data Engineering & Integration Design, develop, test, and support customer data pipelines that ingest, transform, enrich, and distribute customer data across the mParticle ecosystem. Support onboarding of new data sources, attributes, customer events, and downstream activation integrations. Audience Engineering & Activation Build and maintain data pipelines that power audience segmentation, customer traits, profile enrichment, and activation use cases.Implement audience qualification and refresh logic supporting both batch and real-time use cases. Configure and support activation feeds to downstream marketing and personalization platforms, ensuring data accuracy and timeliness. Partner with Product and MarTech teams to implement audience strategies including onboarding, re-engagement, loyalty, churn mitigation, and personalization use cases. Real-Time Data Processing Build scalable solutions using technologies such as Kafka, Spark Structured Streaming, Flink, Dataflow, or equivalent distributed processing platforms. Support event-driven integration patterns that enable low-latency customer activation and personalization scenarios. Assist with implementation and maintenance of eventing platforms such as Kafka, Confluent, or managed messaging services. Data Quality & Operational Excellence Implement automated validation, reconciliation, and quality checks across customer data pipelines. Maintain documentation for data flows, mappings, business logic, and operational procedures. Engineering Delivery Independently manage and deliver complex engineering work items with minimal supervision. Collaborate with team members during sprint planning, solution design, testing, and deployment activities. 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