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Tier-1 brand plus mid-level, generalist Senior Data Engineer title increases candidate competition.
Data engineering skills are broadly transferable across industries, reducing background sensitivity.
Explicit 5-7 years, mandatory PySpark/Spark, cloud and Airflow requirements make filtering strict.
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Architect and develop scalable big data and cloud applications using diverse cloud services to deliver high-performing data products.
Design and implement advanced CI/CD pipelines for automated, reliable software deployments.
Provide technical leadership and mentorship, including delivering top-quality code, documentation, and bridging communication between technical and non-technical stakeholders.
5-7 years of experience designing and building data-intensive, distributed computing solutions with scalable architecture design.
4+ years hands-on experience with Python, PySpark/Hadoop, data/workflow orchestration tools (e.g., Airflow).
Experience deploying data engineering solutions in public clouds like GCP or AWS.
Proficiency with SQL, NoSQL, Apache Spark, Airflow, and operationalizing large-scale batch and stream data pipelines.
Experienced in delivering end-to-end data engineering solutions with a strong focus on cloud-native architectures and automation.
Capable of rapidly prototyping and iterating to meet tight deadlines while maintaining code quality and system documentation.
Effective communicator skilled at translating complex technical requirements for diverse stakeholders and leading teams through mentorship and guidance.