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Tier-1 brand, popular generalist role, mid-level experience and Bangalore location amplify competition.
GCP-specific data engineering requirements moderately limit cross-industry transferability.
Mandatory GCP BigQuery/Dataflow expertise and explicit 5–10 years make shortlisting highly strict.
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Design, build, and maintain scalable batch and streaming data pipelines using Google Cloud Platform services such as BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, and Cloud Storage.
Develop and optimize ETL/ELT frameworks and workflows leveraging Python, SQL, Apache Beam, Spark/PySpark, and orchestrate using Cloud Composer/Airflow for operational readiness including monitoring, troubleshooting, and performance tuning.
Collaborate closely with analytics, AI/ML, architecture, and business teams to support data-driven decision-making, ensure data governance, secure data access, and mentor junior engineers on GCP data platform best practices.
5 to 10 years of work experience as a Data Engineer with hands-on expertise in Google Cloud Platform data services.
Strong experience with BigQuery, SQL, Dataflow, Apache Beam, Dataproc/Spark/PySpark, Cloud Composer/Airflow, Pub/Sub, Cloud Storage, and Python programming.
Bachelor's or Master's degree in Computer Science, Engineering, or related field; MBA or Bachelor of Technology explicitly mentioned.
Mandatory skills include data warehousing concepts, ETL/ELT patterns, data modeling, data quality, production support, IAM/security on GCP, and Agile team experience.
Experienced senior data engineer with deep expertise operating within Google Cloud Platform ecosystems focused on scalable data processing and pipeline orchestration.
Comfortable working cross-functionally with AI/ML teams, architects, and business units to translate complex data needs into reliable production solutions.
Skilled at mentoring junior engineers and driving engineering best practices for cloud-native data platforms while emphasizing cost/performance optimization and data governance.