Kafka：16,829 個職缺
瀏覽與 Kafka 相關的開放職缺。
QA Engineer - Backend (Golang) Middle [Operfeed]
We are looking for a QA Engineer - Backend (Golang) for the Operfeed Team — a team responsible for high-load feed services and the mobile interfaces connected to them. The team develops the core Operfeed platform,…
Senior QA Automation Engineer
Senior QA Automation Engineer in Warsaw automating API and integration tests for digital banking/payment systems using Java, Spring Boot, JUnit, Kafka, Cypress/Nightwatch.js, and Azure DevOps on GCP/GKE within an Agile/Scrum team (hybrid: 3 days in office).
QA Automation Engineer
Design, build, and maintain automated E2E tests for a new multi-tenant insurance platform, verifying data flow between a React/TypeScript frontend and a Java/Spring Boot backend plus external integrations. Stack: React, TypeScript, Java, Spring Boot, PostgreSQL, S3, Kafka/RabbitMQ.
Test Manager for a Banking Project
Czym będziesz się zajmować? Responsibilities: Lead and coordinate QA activities across the Agile Release Train and multiple Scrum teams. Plan, execute, document and report functional, non-functional, system,…
Senior Data Engineer
Design and build scalable cloud-native data platforms from greenfield to production, including near-real-time event-driven pipelines, Data Lake/Lakehouse architectures, and AI-ready infrastructure. Core stack: Python, SQL, Apache Spark/PySpark, Databricks, Kafka, Airflow, Azure/AWS/GCP, Terraform.
Data Engineer (Middle / Strong Middle)
Data Engineer at DareBay, a UGC marketplace platform connecting brands with content creators, owning and scaling the data infrastructure. Builds batch/streaming ETL/ELT pipelines using Python, advanced SQL, Spark/Flink, Airflow, dbt, Snowflake/BigQuery/Redshift, Kafka, Docker, and Kubernetes.
Data Engineer (команда RecSys)
Build and maintain batch ETL pipelines (Airflow) and real-time stream processing (Spark Streaming, Kafka) for analytical data marts and ML models, and grow the feature store. Stack: Python, SQL, Airflow, Spark, Kafka, ClickHouse, MongoDB.
Senior Data Engineer (Отдел разработки DWH и Data инженерии)
Senior Data Engineer at a bank building a Lakehouse platform on Spark + Iceberg, developing ETL/ELT pipelines in Apache Airflow, and writing high-performance analytical queries with Trino for a hybrid team in Almaty.
Middle DevOps инженер
Middle DevOps engineer at KMF Bank in Almaty. Day-to-day work involves implementing DevOps methodology, building CI/CD pipelines, administering Linux servers, and automating infrastructure using Kubernetes, Docker, GitLab CI, and a broad monitoring/observability stack.
Senior Data Engineer
Designs and builds data pipelines and models in Snowflake for BlackLine's AI-powered Invoice-to-Cash SaaS platform, then surfaces trusted insights through Power BI. Works hands-on with SQL, Python, dbt/Airflow, and AI development tools (Cursor, Claude) within a BI & Analytics Engineering team.
Data Engineer - Senior 2
Senior data engineer at Cummins leading the design, development, and maintenance of a cloud-based data and analytics platform. Builds scalable data pipelines, data products, and AI/ML-ready datasets using Spark, Hadoop, SQL, Kafka, and modern ETL/ELT tools across Supply Chain, Finance, and Product domains.
Data Engineer - Senior 1
Senior Data Engineer at Cummins leading design and development of enterprise data pipelines and analytics platforms using Spark, Hadoop, Kafka, cloud data warehouses, and ETL/ELT tools, supporting analytics, AI/ML, and GenAI use cases across Supply Chain, Finance, and other domains.
Data Engineer 9
Data Engineer building and maintaining enterprise data pipelines, ETL/ELT transformations, and curated datasets across Supply Chain, Quality, Finance, and Product Lifecycle domains to power analytics, automation, and GenAI use cases using cloud big-data platforms.
Data Engineer 5
Data Engineer at Cummins building and maintaining enterprise-scale data pipelines, ETL/ELT transformations, and governed data products on cloud and Big Data platforms to support analytics, AI/ML, and GenAI use cases across Supply Chain, Quality, Finance, and Product Lifecycle domains.