Job Description

Key Responsibilities:

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)

Required Qualifications:

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

Preferred Qualifications:

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

Job Tags

Traineeship

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