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Metro locations and popular Data Engineer title increase applicant density, but niche lakehouse skills moderate competition.
Data engineering skills (AWS, pipelines, streaming) are broadly transferable across industries, so low sensitivity.
Specific AWS, Iceberg, streaming, and governance requirements increase technical screening rigor.
Design and build scalable, reusable data pipelines and curated datasets on AWS-based lakehouse architecture using batch and streaming data.
Develop and optimize open table format datasets (e.g., Apache Iceberg) focusing on schema evolution, partitioning, and performance across multiple query engines and cloud providers.
Implement and standardize medallion layered ingestion and transformation patterns (raw, silver, gold) ensuring discoverability, trustworthiness, and governance integration of datasets.
Experience with AWS-native services like S3, Glue, Kinesis and orchestration tools such as Airflow.
Hands-on experience with open table formats such as Apache Iceberg or similar for transactional lakehouse implementations.
Work Experience Required: Not explicitly mentioned in the JD.
Location requirement: Hyderabad or Bangalore.
Experienced in building data platforms that leverage open standards to enable cloud-agnostic and multi-engine interoperability.
Familiar with medallion data architecture and implementing robust data ingestion and transformation pipelines in batch and streaming contexts.
Capable of driving engineering standards across a platform and integrating metadata, lineage, and governance for data trust and discoverability.