Develop high-quality software solutions using Python/Java programming language.
Develop high-performance data pipelines for real-time and batch processing on Lakehouse.
Collaborate with cross-functional teams to understand project requirements and translate them into technical specifications.
Design and implement complex, scalable, and reliable systems that meet business requirements.
Troubleshoot, debug, and optimize pipelines on Lakehouse to ensure performance and reliability.
Stay up-to-date with the latest industry trends, tools, and technologies.
Work closely with DevOps to deploy and maintain applications using Docker and Kubernetes.
Utilize cloud platforms such as AWS to build, deploy, and scale applications.
Integrate and utilize various cloud services to enhance system functionality.
Energetic self-starter, with a desire to work in a startup environment.
Experience in Bigdata processing using any of Spark, Spark Streaming, Flink, Kafka, S3 and Lakehouse – Hudi/Iceberg/DataLake.
Proficient in advanced Java/Python programming skills.
Should have worked for cloud based applications, built on microservices based paradigms, achieving horizontal scaling.
Good hands on and understanding of Elasticsearch, SQL, NoSQL DB (e.g. mongoDB etc), Kubernetes.
Good hands on in Complex Event Processing systems.
Good to have solved scale and performance issues with respect to cloud applications.
Experience of debugging applications running on Unix like systems (e.g. Ubuntu, CentOS).
Experience developing RESTful APIs for complex data sets.
Knowledge of container based development & deployment (e.g. Dockers, rkt) ∙ Exposure to AWS, Google Cloud Platform, Microsoft Azure.
Expertise in software security domain, a plus.
Notice: Concentric AI never asks for money nor paid certifications during the interview process; such behavior is a known scam of which we’ve been made aware.
The ideal candidate is a Python expert with hands-on experience in automation frameworks, CI/CD, and Linux systems. This role requires a strong understanding of microservices and Kubernetes, with exposure to cloud infrastructure and system-level automation.
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