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Large global brand, mid-level generalist role in a metro location increases applicant density.
Core data engineering skills (PySpark, Databricks, SQL) are widely transferable across industries.
Mandatory 4+ years with PySpark/Databricks and specific pipeline/orchestration skills raises filter strictness.
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Lead and implement data lake migration from on-premises to Databricks on Google Cloud Platform.
Develop and optimize data pipelines using Spark (PySpark), Python, SQL in a distributed system environment.
Collaborate cross-functionally with product management and business teams to define requirements and ensure adherence to development best practices including CI/CD and documentation.
3-5 years of development experience including 4+ years with Spark (PySpark), Python, SQL.
Hands-on experience with Databricks development and Linux OS.
Experience with scheduling and orchestration tools (e.g., Databricks Workflows, Airflow).
Bachelor’s degree in Computer Science or related field with minimum 3 years of relevant experience.
Demonstrated ability to migrate and build data pipelines in cloud environments, specifically GCP and Databricks.
Experience working in Agile development environments with CI/CD practices and version control (GIT).
Familiarity with distributed systems design and modern data engineering tools (Docker, Kubernetes, Kafka) is a plus but not mandatory.