Job Description

Job Summary

highly skilled Senior Data Engineer – Kafka Streaming to design, build, and support scalable real-time data solutions that enable analytics, operational reporting, AI, machine learning, and event-driven business processes across the enterprise.

This role will serve as a technical leader for the organization's streaming data platform, with a primary focus on Confluent Kafka, event-driven architecture, real-time integration, and streaming data pipelines. The engineer will be responsible for designing and implementing highly reliable, scalable, and observable streaming solutions that connect Connected Vehicles, manufacturing applications, IoT devices, and other enterprise applications.

The Data & Analytics (D&A) team encompasses Data Engineering, Business Intelligence, Data Science, Master Data Management, and AI. This role will partner closely with application teams, enterprise architects, integration teams, platform engineers, and analytics professionals to establish real-time data movement patterns that complement Polaris's modern data platform consisting of Snowflake, Confluent Kafka, Azure, and Microsoft Fabric.

In addition to streaming platform development, this role will contribute to enterprise data engineering initiatives including data modeling, platform optimization, testing, DevOps, data quality, and AI-enabled engineering practices.

A successful candidate has deep expertise in Kafka and streaming architectures, strong data engineering fundamentals, experience operating production-grade event platforms, and a passion for building highly reliable, scalable, and reusable data integration capabilities

Key Responsibilities

Streaming Data Engineering & Event Architecture

• Design, develop, and maintain enterprise-scale streaming data pipelines using Confluent Kafka and related tools
• Build event-driven integrations between enterprise applications, operational systems, cloud platforms, and analytics environments
• Design Kafka topic structures, schemas, partitioning strategies, retention policies, and message contracts
• Develop real-time ingestion pipelines supporting operational analytics, AI, machine learning, and data product use cases
• Implement publish-subscribe, event sourcing, CDC, and streaming integration patterns
• Develop reusable frameworks and standards for event-driven application integration
• Partner with application and integration teams to define enterprise event architecture standards
• Ensure secure, reliable, and scalable movement of high-volume business-critical data across platforms

Confluent Platform Administration & Engineering

• Develop and support solutions utilizing the Confluent platform ecosystem
• Design and implement Kafka Connect integrations, connectors, and stream processing solutions

Configure and optimize topics, brokers, replication factors, partitions, retention settings, and security controls

Skill Requirements

• 8+ years of experience in Data Engineering, Data Integration, or related disciplines
• 5+ years of hands-on experience with Confluent Kafka or Apache Kafka in enterprise environments
• Strong experience designing and supporting event-driven architectures and real-time data pipelines
• Experience implementing enterprise-scale streaming platforms supporting business-critical workloads
• Experience with Confluent Kafka, Kafka Connect, schema registry, Kafka streams, event-driven architectures, CDC, and real-time data integration
• Strong SQL and data transformation skills
• Experience with Snowflake or similar MPP databases
• Experience with Azure Data Factory, Azure Data Lake, Synapse, Microsoft Fabric, or equivalent cloud data platforms
• Strong understanding of dimensional modeling, medallion architecture, and semantic layer concepts
• Experience with API integration and modern integration architectures
• Familiarity with Python, Spark, Java, Scala, or other development languages used in streaming environments
• Experience with Agile delivery methodologies and product-oriented operating models

Strong understanding of data quality frameworks, observability, monitoring, and operational excellence practices

Other Requirements

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