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Senior Big Data Software Engineer (AWS + Databricks)

SoftserveУдалённо · Remote

  • Strong experience in Big Data, Data Engineering, or related fields, with 5+ years of professional experience
  • Strong proficiency in Python and SQL, with Java experience considered a plus
  • Strong hands-on experience with Apache Spark, distributed data processing, and Databricks
  • Solid experience developing data solutions on AWS using services such as AWS Glue, Amazon Athena, and Amazon Aurora
  • Practical experience working with relational databases such as PostgreSQL and MySQL
  • Familiarity with workflow orchestration technologies such as Apache Airflow and streaming technologies such as Apache Kafka
  • Solid experience with CI/CD, version control, and Infrastructure as Code using Terraform or similar technologies
  • Familiarity with GenAI, MLOps, machine learning workflows, or AI platform integrations is considered a plus
  • Upper-intermediate or higher English proficiency for collaboration across teams
  • Design, develop, and maintain scalable batch and distributed data processing pipelines to support reliable data processing and integration
  • Build and optimize data processing solutions, taking ownership of performance, reliability, scalability, and cost efficiency across data pipelines
  • Collaborate with engineering, AI, and product teams to understand requirements and support analytics, automation, and AI-enabled use cases
  • Develop data solutions using Python, SQL, Apache Spark, Databricks, and AWS services such as AWS Glue, Amazon Athena, and Amazon Aurora
  • Work with relational databases such as PostgreSQL and MySQL to support data processing and integration workflows
  • Develop and maintain workflow orchestration using technologies such as Apache Airflow and contribute to reliable data processing workflows
  • Contribute to CI/CD, Infrastructure as Code, testing, troubleshooting, and production support activities using Terraform or similar technologies
  • Participate in architecture discussions and contribute to the continuous improvement of data platforms and engineering practices

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