Job Description

• Design, develop, and optimize scalable data pipelines using Databricks and PySpark
• Build and maintain ETL/ELT workflows for both structured and unstructured datasets
• Collaborate with stakeholders to gather requirements and translate them into technical solutions
• Architect and implement data models (dimensional modeling, star schema, snowflake schema)
• Improve data quality, reliability, and standardization across systems
• Orchestrate workflows using Apache Airflow (Astro preferred)
• Contribute to the design and evolution of a modern lakehouse architecture
• Support analytics, reporting, and downstream data consumption needs
• Implement data governance, validation, and monitoring frameworks
• Drive performance tuning and optimization of data pipelines and queries
• Mentor junior engineers (for Lead-level candidates)

• 8+ years (Senior) / 10+ years (Lead) of experience in Data Engineering
• Strong hands-on experience with Databricks and PySpark (must-have)
• Proficiency in Python and SQL
• Proven experience building and maintaining data pipelines at scale
• Hands-on experience with workflow orchestration tools (Airflow preferred)
• Strong understanding of data modeling techniques
• Experience working with structured and unstructured data formats (JSON, CSV, XML)
• Excellent problem-solving and analytical skills
• Strong communication skills with the ability to collaborate across technical and business teams

• Experience with Google Cloud Platform , especially BigQuery
• Exposure to DBT (Data Build Tool)
• Experience with lakehouse architecture and modern data platforms
• Background in modernizing or rebuilding legacy data environments
• Experience in startup, consulting, or fast-paced product environments
• Familiarity with data governance and data quality frameworks

For applications and inquiries, contact: hirings@openkyber.com

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