Job Description

To succeed in this role, following professional experience will play a key role: Master\'s degree in either software engineering, data engineering, data science, computer science, physics, statistics, mathematics, or a related field Data engineering professional experience Previous hands-on professional experience in developing and maintaining data pipelines on AWS and Databricks: Lake house architecture based on Databricks and Delta Lake, multi-hop medallion architecture to divide the data lake into bronze, silver and gold layers based on the quality and reusability of the data stored there, data product publishing in Unity catalog. Ability to write compelling code, technical documentation and visualize your technical design. Coding proficiency with multiple languages Python, Scala and DBT. Fluent in industry-standard DevOps practices. Practical experience in working with enterprise data landscapes and data structures: Structural data, non-structural data, metadata, master data, transactional data, batch/NRT. Tech stack: AWS, Gitlab, Databricks for ETL, Airflow, Python, Scala, and DBT for developing ETL, jobs, AWS CDK and Terraform for IaC. Experience on enterprise data sources: Experience in woring with e.g. SAP ERP, Salesforce and product data management (PDM), manaufacturing execution system (MES) system data. Way of working professional experience Passion to utilize agile development methodologies and tools (Jira, Confluence, draw.io). Inbuilt cybersecurity awareness. Understanding on data privacy and compliancy regulations. Ability to work in global multi-cultural team and effectively collaborate within the team. Ability to self-organize and be proactive, seek feedback, be courageous and resilient, and have excellent problem-solving skills. Proficiency in spoken and written English language, and strong facilitation and communication skills. Read more on

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