GCP Data Engineer+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 :

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• Google Data Engineer (Overall 5+ Years with 4 years relevant hands-on experience)
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• 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.
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• Good skills in Python Language, PYSPARK
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• Good Skills in Linux
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• Exposure to creation of CI-CD pipelines for promoting big data release deployments and designing log monitoring features.
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• Excellent written and verbal communication skills in English
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• The following skills are good to have, but not a requirement:
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• Must have knowledge in Java Micro Services and Java FSD (Desirable)
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Good-to-Have

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• Experience in On Premise Hadoop technologies (Hive, Sqoop, SPARK, Kafka)
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Hadoop, Spark, Apache Beam

Responsibility of / Expectations from the Role :

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• Data modelling/Data warehouse modernization/Cloud based data lakes
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• Create and maintain optimal data pipeline architecture.
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• Define technical roadmap for data platform modernization and analysis and assessment of the existing data platforms.
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• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
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• 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.
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• Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
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• Define KPIs for successful modernization of data platform and measure the improvement against the KPIs.
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