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Senior level but Tier-1 brand and desirable cloud DWH skills increase competition moderately.
Data engineering skills are transferable across industries but DWH/tool-specific requirements create moderate sensitivity.
Explicit 8-10 years and many mandatory DWH/cloud/ETL technologies make hiring filters strict.
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Design, develop, and maintain scalable data processing platforms and transactional data pipelines for Corporate Functional Data IT.
Lead technical proof-of-concepts with emerging technologies, including AI/ML use cases like NLP to SQL conversational agents, to enhance analytics capabilities.
Ensure high data quality, governance, and collaborate cross-functionally to deliver end-to-end data analytics solutions impacting business-critical decision-making.
8-10 years of experience in Data Warehousing (DWH) with strong understanding of ETL processes and tools.
Bachelor’s degree in Engineering, Technology, or related field.
Expertise in cloud data platforms (e.g., Snowflake) and databases, with experience in ETL tools such as Informatica and DBT.
Proficiency in scripting/programming (Unix shell script, Python, SQL), data streaming technologies (Kafka), and data quality tools (Snowflake DMF).
Experienced technical leader capable of architecting and optimizing complex data pipelines and platforms in large enterprise environments.
Strong hands-on expertise in cloud-native data solutions, AI/ML integrations, and modern DevOps practices (Docker, Kubernetes, CI/CD).
Comfortable working cross-functionally with analytics, engineering, and business teams to deliver impactful, scalable data products.