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PwC brand, Bangalore metro, mid-level generalist data engineer role creates high applicant competition.
Cloud-native AWS data engineering skills are transferable but require specific AWS and EOD processing experience.
Explicit 5–8 years plus mandatory AWS, SQL, Python, PySpark and production-readiness increases shortlisting strictness.
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Design, build, and optimize scalable ETL/ELT data pipelines using AWS services such as Glue, Step Functions, Lambda, and DMS.
Implement ingestion strategies ensuring referential integrity, correct processing order, idempotency, and recovery mechanisms for high-volume data workloads.
Develop monitoring, validation, and reconciliation frameworks for production data pipelines handling EOD ingestion workloads.
5–8 years total experience with minimum 3 years in data engineering, data integration, or related roles.
Expert-level SQL development and performance optimization skills.
Strong proficiency in SQL, Python, and PySpark with hands-on experience in ETL pipelines and orchestration frameworks.
Experience with AWS cloud-native services such as Glue, Step Functions, Lambda, and DMS; knowledge of ACID-compliant data ingestion and CDC patterns.
Experience owning production readiness and end-to-end delivery of resilient, idempotent ingestion frameworks in high-volume, time-sensitive environments.
Strong operational mindset including capability in monitoring, alerting, and maintaining runbooks.
Exposure to advanced data engineering concepts such as schema evolution, event-driven architectures (e.g., Kafka), and distributed PostgreSQL systems is a plus.