GCP Data Engineer+Pyspark
Summary
GCP Data Engineer role focused on building and maintaining data pipeline architecture, data warehouse modernization, and cloud-based data lakes using Google Cloud Platform big data technologies and PySpark.
Greetings from TCS Recruitment Team!
*Face to Face* Interview For all those GCP Data Engineer+Pyspark we are coming bigger with the plan of Face to Face Drive on 22nd August ,2026 (Saturday) in Bangalore
It is a Face to Face interview planned to attract great Talents in GCP Data Engineer+Pyspark. We believe that your skills and expertise are a better match for the skills we are looking for.
Skill: GCP Data Engineer+Pyspark (Face to Face)
Years of experience: 5+
Location: Bangalore
Date: 22nd August ,2026 (Saturday) (Face to Face)
Drive Time: 9AM to 2 PM
Job Description :
- Google Data Engineer (Overall 5+ Years with 4 years relevant hands-on experience)
- Experience working in GCP based Big Data deployments (Batch/Realtime) leveraging components like GCP Big Query, air flow, Google Cloud Storage, Data fusion, Data flow, Data Proc etc.
- Good skills in Python Language, PYSPARK
- Good Skills in Linux
- Exposure to creation of CI-CD pipelines for promoting big data release deployments and designing log monitoring features.
- Excellent written and verbal communication skills in English
- The following skills are good to have, but not a requirement:
- Must have knowledge in Java Micro Services and Java FSD (Desirable)
Good-to-Have
- Experience in On Premise Hadoop technologies (Hive, Sqoop, SPARK, Kafka)
Hadoop, Spark, Apache Beam
Responsibility of / Expectations from the Role :
- Data modelling/Data warehouse modernization/Cloud based data lakes
- Create and maintain optimal data pipeline architecture.
- Define technical roadmap for data platform modernization and analysis and assessment of the existing data platforms.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and Google cloud ‘big data’ technologies.
- Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
- Define KPIs for successful modernization of data platform and measure the improvement against the KPIs.