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Tier-1 employer and Bengaluru metro increase competition, but senior specialized data engineering role reduces applicant density.
Data engineering skills transfer across industries, though enterprise Lakehouse and aerospace domain preferences add specificity.
Explicit 12+ years requirement plus mandatory Azure Databricks, PySpark, and leadership experience increases filter strictness.
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Architect and implement scalable cloud-based ETL/ELT data engineering solutions using Azure Data Platform technologies including Databricks, PySpark, ADF, and ADLS.
Lead, mentor, and provide technical direction to distributed engineering teams for enterprise data lakehouse and ETL frameworks embracing Medallion Architecture and metadata-driven pipelines.
Collaborate with business, architecture, and global technology teams to deliver governed, high-performance analytics platforms and drive cloud modernization initiatives.
12+ years of experience in data engineering, ETL development, data warehousing, and enterprise data architecture.
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field; Advanced degree with 8+ years also accepted.
Strong hands-on expertise in PySpark, Azure Databricks, Azure Data Factory, ADLS, and related Azure services.
Experience leading engineering teams and delivering enterprise-scale cloud data platforms spanning ETL frameworks, CI/CD pipelines, and metadata-driven orchestration.
Proven ability to lead technical teams and manage delivery with an emphasis on architecture governance and best practices in cloud data engineering.
Deep expertise in Azure-based modern Lakehouse platform design, including Medallion Architecture and Delta Lake concepts.
Experience collaborating closely with cross-functional stakeholders in large enterprises, ideally within aerospace/defense or manufacturing domains in a hybrid work environment.