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 :

• 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.