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Tier-1 brand, mid-level data engineer title, Bangalore metro, and broad AWS/SQL/Python requirements increase competition.
Medium — core data engineering skills transfer across industries, though consulting and enterprise integration experience adds bias.
High due to explicit 5–8 years plus mandatory AWS, SQL, Python, PySpark and production-readiness requirements.
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Design, build, and optimize scalable ETL/ELT data pipelines using AWS services (Glue, Step Functions, Lambda, DMS).
Implement direct-to-Aurora data ingestion strategies ensuring referential integrity, idempotency, and recovery mechanisms.
Develop monitoring, validation, and reconciliation frameworks for managing high-volume, time-sensitive data workflows, especially EOD ingestion workloads.
5–8 years of professional experience with at least 3 years in data engineering or data integration roles.
Expert-level SQL development and performance optimization skills; strong proficiency in Python and PySpark.
Hands-on experience with AWS data engineering tools including Glue, Step Functions, Lambda, and DMS.
Bachelor's or Master's degree in BE/B.Tech/ME/M.Tech/MBA/MCA or equivalent.
Proven experience delivering production-ready, resilient, and idempotent data ingestion frameworks at scale.
Strong operational mindset with expertise in monitoring, alerting, and maintaining runbooks for data pipelines.
Prior exposure to cloud-native architectures and advanced data engineering patterns including ACID-compliant ingestion, schema evolution, and CDC.