HCLTech→
Track Lead - Saviynt, Java at HCLTech in Hyderabad, Telangana, IN
Skills
Job Description
Job Summary
Role: Real-Time, Low-Latency Data Engineer Experience: 5+ Years Location: Flexible/Hybrid Team: Cloud & Data Services (CDS) Role Overview We are seeking a highly skilled Real-Time, Low-Latency Data Engineer to design, develop, and optimize high-performance data platforms that process large-scale streaming data with minimal latency. The ideal candidate will have expertise in modern data engineering practices, distributed systems, event-driven architectures, and cloud-native technologies to enable real-time analytics and business-critical decision-making. Key Responsibilities Design, build, and maintain scalable real-time data pipelines capable of processing millions of events with low latency and high throughput. Develop and optimize streaming data solutions using technologies such as Apache Kafka, Apache Flink, Spark Streaming, Dataflow, or equivalent platforms. Implement event-driven architectures and data ingestion frameworks for near real-time analytics and operational use cases. Collaborate with data scientists, application teams, product owners, and business stakeholders to understand data requirements and deliver robust solutions. Optimize data processing performance, reliability, fault tolerance, and system observability. Design and implement data models, schemas, and storage solutions for real-time and historical data consumption. Build monitoring, alerting, and automated recovery mechanisms to ensure platform availability and service reliability. Ensure data quality, governance, security, and compliance standards are embedded within data engineering processes. Participate in architecture reviews, code reviews, and DevOps practices including CI/CD and infrastructure automation. Troubleshoot complex production issues and perform root cause analysis for streaming and distributed systems. Required Skills & Qualifications Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related discipline. 5+ years of experience in data engineering, with significant exposure to real-time streaming data platforms. Strong programming skills in Python, Java, or Scala. Hands-on experience with Kafka, Flink, Spark Streaming, Kafka Streams, or similar stream processing frameworks. Expertise in SQL and NoSQL databases such as PostgreSQL, Cassandra, MongoDB, DynamoDB, or Redis. Strong understanding of distributed systems, message queues, event sourcing, and microservices architectures. Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform. Proficiency in containerization and orchestration technologies such as Docker and Kubernetes. Experience with CI/CD pipelines, Infrastructure as Code (Terraform, ARM Templates, CloudFormation), and DevOps methodologies. Strong problem-solving, analytical, and communication skills. Preferred Qualifications Experience with low-latency trading, IoT, telemetry, fraud detection, or real-time customer analytics platforms. Knowledge of lakehouse architectures, Delta Lake, Iceberg, or Hudi. Exposure to ML feature stores and real-time AI/ML inference pipelines. Industry certifications in cloud, data engineering, or streaming technologies. Success Metrics Reduced data processing latency and improved throughput. High availability and reliability of streaming platforms. Improved data quality, observability, and operational excellence. Faster delivery of data products supporting critical business outcomes. This role is ideal for engineers passionate about building high-performance, real-time data ecosystems that power next-generation analytics and digital experiences.
Key Responsibilities
Role: Real-Time, Low-Latency Data Engineer Experience: 5+ Years Location: Flexible/Hybrid Team: Cloud & Data Services (CDS) Role Overview We are seeking a highly skilled Real-Time, Low-Latency Data Engineer to design, develop, and optimize high-performance data platforms that process large-scale streaming data with minimal latency. The ideal candidate will have expertise in modern data engineering practices, distributed systems, event-driven architectures, and cloud-native technologies to enable real-time analytics and business-critical decision-making. Key Responsibilities Design, build, and maintain scalable real-time data pipelines capable of processing millions of events with low latency and high throughput. Develop and optimize streaming data solutions using technologies such as Apache Kafka, Apache Flink, Spark Streaming, Dataflow, or equivalent platforms. Implement event-driven architectures and data ingestion frameworks for near real-time analytics and operational use cases. Collaborate with data scientists, application teams, product owners, and business stakeholders to understand data requirements and deliver robust solutions. Optimize data processing performance, reliability, fault tolerance, and system observability. Design and implement data models, schemas, and storage solutions for real-time and historical data consumption. Build monitoring, alerting, and automated recovery mechanisms to ensure platform availability and service reliability. Ensure data quality, governance, security, and compliance standards are embedded within data engineering processes. Participate in architecture reviews, code reviews, and DevOps practices including CI/CD and infrastructure automation. Troubleshoot complex production issues and perform root cause analysis for streaming and distributed systems. Required Skills & Qualifications Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related discipline. 5+ years of experience in data engineering, with significant exposure to real-time streaming data platforms. Strong programming skills in Python, Java, or Scala. Hands-on experience with Kafka, Flink, Spark Streaming, Kafka Streams, or similar stream processing frameworks. Expertise in SQL and NoSQL databases such as PostgreSQL, Cassandra, MongoDB, DynamoDB, or Redis. Strong understanding of distributed systems, message queues, event sourcing, and microservices architectures. Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform. Proficiency in containerization and orchestration technologies such as Docker and Kubernetes. Experience with CI/CD pipelines, Infrastructure as Code (Terraform, ARM Templates, CloudFormation), and DevOps methodologies. Strong problem-solving, analytical, and communication skills. Preferred Qualifications Experience with low-latency trading, IoT, telemetry, fraud detection, or real-time customer analytics platforms. Knowledge of lakehouse architectures, Delta Lake, Iceberg, or Hudi. Exposure to ML feature stores and real-time AI/ML inference pipelines. Industry certifications in cloud, data engineering, or streaming technologies. Success Metrics Reduced data processing latency and improved throughput. High availability and reliability of streaming platforms. Improved data quality, observability, and operational excellence. Faster delivery of data products supporting critical business outcomes. This role is ideal for engineers passionate about building high-performance, real-time data ecosystems that power next-generation analytics and digital experiences.
Skill Requirements
Role: Real-Time, Low-Latency Data Engineer Experience: 5+ Years Location: Flexible/Hybrid Team: Cloud & Data Services (CDS) Role Overview We are seeking a highly skilled Real-Time, Low-Latency Data Engineer to design, develop, and optimize high-performance data platforms that process large-scale streaming data with minimal latency. The ideal candidate will have expertise in modern data engineering practices, distributed systems, event-driven architectures, and cloud-native technologies to enable real-time analytics and business-critical decision-making. Key Responsibilities Design, build, and maintain scalable real-time data pipelines capable of processing millions of events with low latency and high throughput. Develop and optimize streaming data solutions using technologies such as Apache Kafka, Apache Flink, Spark Streaming, Dataflow, or equivalent platforms. Implement event-driven architectures and data ingestion frameworks for near real-time analytics and operational use cases. Collaborate with data scientists, application teams, product owners, and business stakeholders to understand data requirements and deliver robust solutions. Optimize data processing performance, reliability, fault tolerance, and system observability. Design and implement data models, schemas, and storage solutions for real-time and historical data consumption. Build monitoring, alerting, and automated recovery mechanisms to ensure platform availability and service reliability. Ensure data quality, governance, security, and compliance standards are embedded within data engineering processes. Participate in architecture reviews, code reviews, and DevOps practices including CI/CD and infrastructure automation. Troubleshoot complex production issues and perform root cause analysis for streaming and distributed systems. Required Skills & Qualifications Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related discipline. 5+ years of experience in data engineering, with significant exposure to real-time streaming data platforms. Strong programming skills in Python, Java, or Scala. Hands-on experience with Kafka, Flink, Spark Streaming, Kafka Streams, or similar stream processing frameworks. Expertise in SQL and NoSQL databases such as PostgreSQL, Cassandra, MongoDB, DynamoDB, or Redis. Strong understanding of distributed systems, message queues, event sourcing, and microservices architectures. Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform. Proficiency in containerization and orchestration technologies such as Docker and Kubernetes. Experience with CI/CD pipelines, Infrastructure as Code (Terraform, ARM Templates, CloudFormation), and DevOps methodologies. Strong problem-solving, analytical, and communication skills. Preferred Qualifications Experience with low-latency trading, IoT, telemetry, fraud detection, or real-time customer analytics platforms. Knowledge of lakehouse architectures, Delta Lake, Iceberg, or Hudi. Exposure to ML feature stores and real-time AI/ML inference pipelines. Industry certifications in cloud, data engineering, or streaming technologies. Success Metrics Reduced data processing latency and improved throughput. High availability and reliability of streaming platforms. Improved data quality, observability, and operational excellence. Faster delivery of data products supporting critical business outcomes. This role is ideal for engineers passionate about building high-performance, real-time data ecosystems that power next-generation analytics and digital experiences.
Other Requirements
Role: Real-Time, Low-Latency Data Engineer Experience: 5+ Years Location: Flexible/Hybrid Team: Cloud & Data Services (CDS) Role Overview We are seeking a highly skilled Real-Time, Low-Latency Data Engineer to design, develop, and optimize high-performance data platforms that process large-scale streaming data with minimal latency. The ideal candidate will have expertise in modern data engineering practices, distributed systems, event-driven architectures, and cloud-native technologies to enable real-time analytics and business-critical decision-making. Key Responsibilities Design, build, and maintain scalable real-time data pipelines capable of processing millions of events with low latency and high throughput. Develop and optimize streaming data solutions using technologies such as Apache Kafka, Apache Flink, Spark Streaming, Dataflow, or equivalent platforms. Implement event-driven architectures and data ingestion frameworks for near real-time analytics and operational use cases. Collaborate with data scientists, application teams, product owners, and business stakeholders to understand data requirements and deliver robust solutions. Optimize data processing performance, reliability, fault tolerance, and system observability. Design and implement data models, schemas, and storage solutions for real-time and historical data consumption. Build monitoring, alerting, and automated recovery mechanisms to ensure platform availability and service reliability. Ensure data quality, governance, security, and compliance standards are embedded within data engineering processes. Participate in architecture reviews, code reviews, and DevOps practices including CI/CD and infrastructure automation. Troubleshoot complex production issues and perform root cause analysis for streaming and distributed systems. Required Skills & Qualifications Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related discipline. 5+ years of experience in data engineering, with significant exposure to real-time streaming data platforms. Strong programming skills in Python, Java, or Scala. Hands-on experience with Kafka, Flink, Spark Streaming, Kafka Streams, or similar stream processing frameworks. Expertise in SQL and NoSQL databases such as PostgreSQL, Cassandra, MongoDB, DynamoDB, or Redis. Strong understanding of distributed systems, message queues, event sourcing, and microservices architectures. Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform. Proficiency in containerization and orchestration technologies such as Docker and Kubernetes. Experience with CI/CD pipelines, Infrastructure as Code (Terraform, ARM Templates, CloudFormation), and DevOps methodologies. Strong problem-solving, analytical, and communication skills. Preferred Qualifications Experience with low-latency trading, IoT, telemetry, fraud detection, or real-time customer analytics platforms. Knowledge of lakehouse architectures, Delta Lake, Iceberg, or Hudi. Exposure to ML feature stores and real-time AI/ML inference pipelines. Industry certifications in cloud, data engineering, or streaming technologies. Success Metrics Reduced data processing latency and improved throughput. High availability and reliability of streaming platforms. Improved data quality, observability, and operational excellence. Faster delivery of data products supporting critical business outcomes. This role is ideal for engineers passionate about building high-performance, real-time data ecosystems that power next-generation analytics and digital experiences.