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Job Description
Structured overview of role & requirementsAbout This Role
Lead design and implementation of scalable, stable data pipelines using stream and batch processing architectures.
Drive improvements in data architecture, quality, monitoring, and availability, integrating new data sources for reliability.
Develop and oversee data quality frameworks, enterprise-wide data dictionary, and monitoring/alerting policies for critical data systems.
Minimum Requirements
5+ years experience with SQL, Python (including PySpark on Databricks), and large-scale distributed computing technologies (Spark/Hive/Hadoop).
Demonstrated experience with Databricks platform optimization and pipeline orchestration.
Experience with CI/CD systems and testing/monitoring data for anomalies and issue resolution.
Work Experience Required: 5+ years; Location and notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced in leading architecture and implementation of end-to-end big data systems in a global, cross-functional environment.
Strong background in distributed computing and data platform optimization, particularly with Databricks and related ecosystems.
Capable of enforcing best practices in coding, source management, and mentoring junior engineers, with knowledge of agile software development lifecycle.