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Tier-1 brand, popular data engineer title, mid-level experience band, and Bengaluru metro location increase competition.
Core data engineering skills (AWS, Spark, ETL) are highly transferable across industries, so sensitivity is low.
Multiple mandatory technical requirements and an explicit 4+ years mandate indicate high shortlisting rigidity.
Design, develop, and troubleshoot software components focusing on data integration and analysis across multiple data stores and formats.
Build and optimize scalable data acquisition and integration solutions using Big Data technologies and AWS services such as EMR, S3, AWS Glue, Lambda, and Apache Airflow.
Collaborate with cross-functional and remote teams utilizing Agile methodology and enterprise-authorized AI-assisted development tools to improve code quality and delivery speed.
4+ years of experience in data integration projects using Big Data technologies and AWS Data Warehousing (EMR, S3, AWS Glue, Lambda, Apache Airflow) with Infrastructure as Code (Terraform or CloudFormation).
Strong experience with CICD tools including Jenkins, Git, Artifactory, Yaml, Maven for cloud deployments.
Hands-on experience with Spark engineering using PySpark or Scala, and knowledge of Big Data querying tools like Athena, RDS, or Databricks SQL Warehouse.
Mandatory skills include Java open-source/API standards, Data Warehousing, Data Modeling, Data Lake concepts, and experience with data formats such as Parquet, ORC, Avro, and table formats Delta Lake or Iceberg.
Experienced in building and maintaining end-to-end scalable data pipelines and integration solutions using cloud-native AWS services and Big Data frameworks.
Proficient in leveraging AI-assisted software development tools critically within a secure, stable application development environment with understanding of responsible AI usage.
Skilled at working in agile teams including cross-functional and distributed groups, enabling effective product development and system design with a focus on operational stability.