Senior Data Engineer - Python/AWS in Pune, भारत

Role: Senior Data EngineerExperience: 7+ YearsLocation: PuneJoining: Immediate to 30 Days' Notice PeriodRole Summary:We are looking for a skilled Sr. Data Engineer with strong expertise in Python and cloud-native technologies to design, develop, and maintain scalable data pipelines and distributed systems. The ideal candidate should have hands-on experience with ETL/ELT pipelines, workflow orchestration, AWS cloud services, data visualization, distributed architectures, and modern data engineering https://jobeax.com/link/TtkK7BHPbancvnFq Responsibilities:- Design, develop, and maintain scalable ETL/ELT data pipelines.- Build and manage distributed and event-driven architectures for large-scale data processing.- Develop backend services and data infrastructure using Python.- Implement DAG-based workflow orchestration using Airflow, Prefect, or Dagster.- Work with AWS services including Lambda, S3, ECS/EKS, SQS, and CloudWatch.- Design and optimize data models and schema structures.- Develop analytics dashboards and reporting solutions using Amazon QuickSight.- Implement CI/CD pipelines and Infrastructure as Code (IaC).- Monitor and maintain data platforms using observability and monitoring tools.- Collaborate with Data Scientists, ML Engineers, and Backend Developers.- Support AWS cloud migration and modernization https://jobeax.com/link/CTgddHUNWTroYPch Skills:- Strong proficiency in Python.- Experience with Docker and Kubernetes.- Strong understanding of distributed and event-driven architectures.- Hands-on experience building ETL/ELT pipelines.- Strong knowledge of data modeling and schema design.- Experience with Airflow, Prefect, or Dagster.- Hands-on experience with AWS Lambda, S3, ECS/EKS, SQS, and CloudWatch.- Experience with CI/CD and Infrastructure as Code (IaC).- Hands-on experience with Amazon QuickSight for dashboards, reporting, and https://jobeax.com/link/RuPwATTO2ckHqbM4 to Have:- AWS Application Migration Service (MGN/AMC) or similar migration technologies.- Logstash or other data ingestion frameworks.- OpenSearch / Elasticsearch.- Kafka / Kinesis and streaming technologies.- Prometheus, Grafana, or ELK Stack.- MLOps lifecycle knowledge.- Vector databases and embedding pipelines.- Large-scale indexing and search optimization.- RAG, Natural Language Search, GenAI, knowledge graphs, prompt engineering, and embedding models.- Knowledge of Golang, Rust, Java, C/C++, or other compiled languages. (ref:hirist.tech)