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Mid-level Bangalore data engineer with common Spark/Airflow/GCP skills yields moderate applicant competition.
Core data engineering skills (Spark, Airflow, GCP) are highly transferable across industries.
Mandatory 2+ years plus core Spark, Airflow, Python, and GCP skills enforce strict technical filtering.
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Design, develop, and deliver high-performance batch and streaming data pipelines using Apache Spark and Apache Airflow on Google Cloud Platform.
Collaborate on defining and evolving the technical data roadmap and adopt cloud-native data architecture best practices.
Implement and maintain CI/CD pipelines and automate operations for scalable, reliable, and cost-effective cloud-hosted data workloads.
Minimum 2 years professional experience in building and operating enterprise-grade software or data applications.
Hands-on experience with distributed data processing frameworks like Apache Spark and orchestration tools like Apache Airflow.
Proficiency in Python for data pipeline development and scripting.
Bachelor's degree in Computer Science, Data Engineering, or a related field (Master's preferred).
Experience working with cloud-native data engineering on Google Cloud Platform including services like BigQuery, Cloud Composer, Dataproc, Pub/Sub, GKE.
Familiarity with modern data stack tools and data modeling techniques applicable to large scale data warehouses and lakehouse architectures.
Comfortable contributing to DataOps practices including CI/CD, automation, monitoring, and cost-aware cloud infrastructure management.