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Tier-1 brand, mid-level generalist data role in metro with common tech stack increases candidate competition.
Core data engineering skills (Python, Spark, Databricks, SQL) are broadly transferable across industries.
Mandatory 3+ years and specific Databricks/Spark/Python/SQL requirements make shortlisting highly strict.
Design, develop, and maintain scalable ELT data pipelines and data architectures using Python, Spark/PySpark, Databricks, and SQL.
Ensure pipeline performance, reliability, maintainability, and data security by applying engineering best practices and supporting governance frameworks.
Collaborate with stakeholders to translate data requirements into production-ready solutions and improve pipeline stability through SDLC practices including CI/CD and testing.
3+ years of applied software engineering experience with formal training or certification.
Hands-on experience with Databricks, Spark/PySpark, Python, and SQL.
Experience in developing and maintaining data pipelines and data processing systems with understanding of data lifecycle and cloud platforms (AWS).
Experience with SDLC practices including CI/CD, testing, deployment, and usage of enterprise-authorized AI-assisted software development tools in production.
Experienced in building and optimizing complex data pipelines within agile teams and capable of troubleshooting data and pipeline issues effectively.
Skilled in using AI-assisted coding tools critically, ensuring outputs meet correctness, performance, and security standards.
Knowledgeable in responsible AI use, data sensitivity, secure handling practices, and able to guide peers on safe AI tool usage.