Kafka to AWS MSK Migration Engineer

Lorven Technologies Inc.; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Lorven Technologies Inc. in Bengaluru is seeking a seasoned engineer to lead Kafka-to-MSK migrations. The role involves assessing on-prem clusters, planning MSK provisioning, and guiding migration readiness across teams.

You will run UAT and production cutovers, optimize MSK performance, and build automation to enable repeatable migrations. Strong Unix, Python/Java skills and experience with MirrorMaker 2 are essential, along with collaboration across infrastructure and application teams.

Qualifications

• Education: Bachelor's or Master's in CS/Engineering/Math or related quantitative discipline.
• 3–5 years hands-on engineering with distributed messaging systems.
• Proficiency in Python, Java, or similar scripting language.

Responsibilities

• Assess on-prem Kafka clusters and determine migration readiness.
• Analyze workloads to determine MSK placement and configuration.
• Plan and provision AWS MSK infrastructure capacity.
• Execute UAT and production parallel testing using MirrorMaker 2.
• Perform performance tuning and optimization of MSK clusters.
• Collaborate with app, infra, and platform teams for migration plans and cutover.
• Manage release readiness and production cutover activities.
• Support legacy on-prem Kafka decommissioning after migration.
• Design automation frameworks for large-scale Kafka migrations.
• Document procedures, learnings, risks, and best practices.

Skills

Analytical thinking

Troubleshooting

Ownership mindset

Clear communication

Cross-team collaboration

Education

Bachelor's or Master's degree in CS/Engineering/Math

Tools

AWS MSK

Apache Kafka

MirrorMaker 2

Confluent Replicator

Kubernetes

RESTful APIs

GitLab

Jenkins

Maven

Prometheus

Grafana

Splunk

Schema Registry

Kafka Connect

IAM Policies

Job description

Key Responsibilities

• Assess on-prem Kafka clusters and perform discovery across topics, partitions, consumer groups, throughput patterns, retention policies, and inter-service dependencies to determine migration readiness.


• Analyze workloads based on message volume, business criticality, ordering guarantees, latency requirements, and consumer group complexity to determine appropriate MSK cluster placement and configuration.


• Plan and provision required AWS MSK infrastructure capacity including broker sizing, storage, networking, and security configurations to support migration, testing, and production execution.


• Execute UAT and production parallel testing using MirrorMaker 2 or similar replication tooling, compare message delivery outcomes, capture evidence, and troubleshoot discrepancies through closure.


• Perform performance tuning and optimization of MSK clusters to ensure migrated workloads are stable, scalable, efficient, and production ready.


• Partner with application engineering, infrastructure, and platform teams to finalize migration plans, consumer/producer cutover approach, validation criteria, and rollback considerations.


• Manage release readiness, execute production cutover activities including consumer group migration, offset synchronization, and complete post-release checkout and validation procedures.


• Support legacy on-premises Kafka cluster decommissioning after successful migration and validation.


• Design, enhance, and implement automation frameworks and AI-assisted solutions to enable repeatable, efficient, large-scale Kafka-to-MSK migrations.


• Document migration procedures, operational learnings, risks, and best practices to improve the factory execution model.

Required Qualifications

Basic Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Engineering, Applied Mathematics, or a related quantitative discipline.


• Experience: 3–5 years of hands‑on engineering experience in a collaborative, team‑based environment with distributed messaging systems.


• Programming: Professional proficiency in Python, Java, or a similar programming/scripting language.


• Systems: Strong Unix/Linux fundamentals with the ability to troubleshoot application, deployment, environment, and runtime issues.


• Methodology: Familiarity with SDLC practices, CI/CD delivery models, change management, and Kubernetes-based deployments.

Technical Competencies

Candidates are not expected to be experts in every tool, but should bring strong hands‑on experience across several of the following areas:

• AWS Services: Hands‑on experience with AWS compute and migration patterns, including ECS, EKS, Lambda, CloudWatch, and related cloud services.


• Apache Kafka: Deep hands‑on experience with Kafka architecture including brokers, topics, partitions, consumer groups, replication, and cluster operations.


• AWS MSK: Practical experience with Amazon Managed Streaming for Apache Kafka including cluster provisioning, configuration, monitoring, and security (IAM, mTLS, SASL/SCRAM).


• Migration Tooling: Experience with MirrorMaker 2, Confluent Replicator, or similar cross‑cluster replication tools for data migration and offset synchronization.


• Cloud Migration: Practical understanding of on‑prem to cloud migration strategies, workload assessment, testing, cutover, and post‑migration validation.


• Kafka Ecosystem: Familiarity with Schema Registry, Kafka Connect, Kafka Streams, and related ecosystem components.


• Networking and Security: Understanding of VPC peering, PrivateLink, TLS encryption, IAM policies, and network connectivity patterns for hybrid Kafka architectures.


• Programming and Automation: Experience with Python, Java, Shell, or similar languages to build scripts, utilities, and automation frameworks for topic creation, ACL migration, and consumer group management.


• APIs and Integration: Working knowledge of RESTful APIs, API design, and API Gateway patterns.


• CI/CD and Version Control: Strong experience with GitLab, Jenkins, Maven, or similar tools supporting automated build, test, and deployment pipelines.


• Observability: Familiarity with monitoring, logging, alerting, and troubleshooting using tools such as CloudWatch, Prometheus, Grafana, Splunk, or equivalent platforms for Kafka/MSK cluster health.


• Automation Excellence: Ability to design repeatable automation and apply AI‑assisted engineering approaches to improve migration scale, quality, and efficiency.

Core Competencies

• Exceptional analytical, troubleshooting, and debugging skills.


• Strong ownership mindset with the ability to drive work to closure and meet commitments.


• Clear written and verbal communication, including concise status updates, structured briefings, and proactive stakeholder management.


• Effective collaboration across application, infrastructure, platform, and global engineering teams.


• Ability to work constructively across time zones, build alignment, and resolve issues with urgency and professionalism.


• Strong integrity, sound judgment, and commitment to good conduct and ethical decision‑making.


• High energy,