GCP Data Engineer With Pyspark Java

Greetings from Tata Consultancy Services Recruitment Team!

*Face to Face* Interview For all those GCP DevOps Engineer With Pyspark Java 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 DevOps Engineer With Pyspark Java. We believe that your skills and expertise are a better match for the skills we are looking for.

Skill: GCP DevOpS Engineer With Pyspark Java (Face to Face)

Years of experience: Minimum 5 years of relevant experience required

Location: Bangalore

Date: 22nd August ,2026 (Saturday) (Face to Face)

Drive Time: 9 AM 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.