Google Cloud Platform Data Engineer

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Role Overview

GCP Data Engineer

Key Responsibilities

• Develop, test, and maintain data acquisition pipelines for large volumes of structured and unstructured data (batch and real-time) on GCP.
• Build scalable and optimized datasets in alignment with business requirements.
• Construct end-to-end big data pipeline architectures.
• Collaborate with business stakeholders to identify opportunities for data acquisition and translate requirements into technical specifications.
• Work extensively with GCP tools such as

  Python, SQL, DataFlow, DataStream, CDC (Change Data Capture), Cloud Functions, Cloud Run, Pub/Sub, BigQuery, Cloud Storage

  .
• Use

  SAP SLT

  to replicate SAP tables into GCP.
• Develop

  JSON-based messaging structures

  to integrate with multiple applications.
• Implement

  DevOps & CI/CD practices

  (GitHub, Terraform) to ensure reliability, scalability, and automation of pipelines.
• Ensure data security and compliance by implementing

  IAM roles, Policy Tags, and authorized views

  in BigQuery.
• Design and implement ingestion pipelines using REST APIs.
• Monitor, troubleshoot, and optimize pipelines with logs and alerts.
• Recommend and drive improvements to enhance data quality, reliability, and efficiency.

Required Skills & Experience

• 5+ years

  of experience as a Data Engineer with a focus on Google Cloud Platform.
• Minimum

  1 year hands-on

  experience with key GCP tools (Python, SQL, DataFlow, DataStream, Cloud Functions, Cloud Run, Pub/Sub, BigQuery, Cloud Storage).
• Strong understanding of

  data ingestion, CDC techniques, and real-time/batch pipeline orchestration

  .
• Experience with

  SAP SLT

  for replicating SAP data into GCP (preferred).
• Solid knowledge of

  data partitioning, clustering, and governance

  in BigQuery.
• Exposure to

  DevOps/CI/CD tools

  (GitHub, Terraform).
• Strong problem-solving and communication skills.