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Senior-level role at a known multinational in a metro market creates moderate applicant competition.
Core data engineering skills listed are broadly transferable across industries, so background sensitivity is low.
Explicit 9–14 years requirement and mandatory Snowflake/BigQuery/Databricks/PySpark/cloud skills make filters strict.
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Design, implement, and manage scalable data architecture including pipelines, warehouses, and lakes for high volume, high dimensional data.
Ensure data quality, availability, security, and compliance with governance standards for analysis and reporting.
Collaborate with product owners, data scientists, and IT teams to support complex data science and analytics projects in the VE/CE organization.
9 to 14 years of experience in Data Engineering, ETL Development, Database Administration.
Proficiency in Snowflake, Oracle, Big Query, Azure Databricks, Google Cloud, and CI/CD & DevOps processes.
Expertise in Python, SQL, PySpark; experience with both structured and unstructured data.
Experience in data modeling for relational and NoSQL databases; knowledge of data governance and security standards.
Experienced in handling complex data engineering tasks in cloud environments (Azure, Google Cloud) with advanced ETL and data modeling skills.
Able to lead technical vision and mentor peers while collaborating cross-functionally with product owners, data scientists, and IT support.
Familiarity with Agile methodologies and modern data integration, as well as exposure to NoSQL systems and advanced ML/AI-related tools.