Bengaluru, Karnataka
Job Summary

JOB SUMMARY: Polaris is seeking a 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. While this position reports through the D&A; organization, the individual will be embedded within the Polaris Ride Command team and will work closely with Ride Command product, engineering, mobile, web, platform, and connected vehicle teams. The role will support the real-time data and event streaming needs of the Ride Command ecosystem, enabling connected rider experiences, vehicle telemetry processing, mobile and web integrations, and downstream analytics capabilities. 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 Implement schema management and governance using Schema Registry Design monitoring, alerting, and operational support capabilities for Kafka environments Troubleshoot platform, connectivity, throughput, latency, and reliability issues Partner with platform engineering teams to improve scalability, resiliency, and performance Data Integration & Enterprise Data Movement Build and maintain real-time and near-real-time integration patterns between enterprise systems Design hybrid architectures that combine streaming, CDC, API-based, and batch processing approaches Implement reliable change data capture strategies and event-driven synchronization patterns Ensure seamless integration between streaming platforms and the Enterprise Data Warehouse ecosystem Data Quality & Reliability Define and implement data quality controls within streaming architectures Develop automated validation, reconciliation, monitoring, and exception handling capabilities Identify and prevent duplicate, missing, late-arriving, or corrupt data conditions Implement observability frameworks for data movement, processing status,



and pipeline health Build automated alerting and remediation processes for production support Data Modeling & Analytics Enablement Support development of dimensional models and curated datasets consumed by BI, Data Science, AI, and business analytics teams Design streaming ingestion architectures aligned with existing medallion architecture standards Ensure effective integration of streaming data into Snowflake, data lake, Fabric, and semantic model environments Partner with analytics teams to identify opportunities for real-time reporting and decision support Performance Optimization Optimize Kafka throughput, consumer performance, latency, and scalability Tune streaming applications and connectors to improve reliability Data Integration & Enterprise Data Movement Build and maintain real-time and near-real-time integration patterns between enterprise systems Design hybrid architectures that combine streaming, CDC, API-based, and batch processing approaches Implement

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
Solid 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
Corporate office environment – fast-paced
Operate with minimal supervision
Participate in critical production support and escalation activities as needed
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📌 Senior Technical Lead (India)
🏢 HCLTech
📍 India