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Business Title GCP Data Engineer Vertex AI Years of Experience: 4 to 7 years Shift timing 12 PM - 10 PM Location : Mumbai, Pune, Bangalore Immediate to 20 Days Joiners only. Key responsibilities: Creates and maintains optimal data pipeline architecture Assembles large, complex data sets that meet functional / non-functional business requirements Identifies, designs and implements internal process improvements: automating manual processes, optimising Title: GCP Data engineer Location - Pune, Bengaluru, Mumbai About the Role We are seeking a highly motivated and hands-on GCP Data Engineer to support the development and delivery of modern enterprise data and AI platforms on Google Cloud Platform (GCP). This role will work closely with Lead Data Engineers, Enterprise Architects, analytics teams, and AI/ML teams to build scalable, reliable, and AI-ready data pipelines and cloud-native data solutions that support enterprise analytics, intelligent automation, and digital transformation initiatives. The ideal candidate should possess strong foundations in cloud data engineering, distributed data processing, and modern data platform development, along with a passion for building high-quality, scalable, and reusable data solutions in a fast-paced enterprise environment. This role offers an excellent opportunity to work on large-scale data modernization, semantic data enablement, and next-generation AI data ecosystems. Key Responsibilities 1. Cloud Data Engineering & Pipeline Development Develop and maintain scalable batch and real-time data pipelines on GCP. Build ingestion, transformation, and serving pipelines supporting enterprise analytics and AI use cases. Assist in modernization of legacy data workflows into cloud-native architectures. Develop reusable and maintainable data engineering components following established architectural standards. Support implementation of event-driven and streaming-based data processing solutions. 2. Data Product Development Contribute to development of reusable and domain-oriented data products. Implement data transformation logic and standardized data models supporting downstream analytics and AI consumption. Support implementation of Data quality validations Schema management Metadata enrichment Data contracts Reusable transformation frameworks Ensure data pipelines are reliable, scalable, and production-ready. - GCP Platform Development Work with GCP-native services including: BigQuery Dataflow Dataproc DBT Pub/Sub Cloud Storage Cloud Composer (Airflow) Cloud SQL Develop ETL/ELT pipelines and optimize data processing workloads. Support orchestration and scheduling of enterprise data workflows. Monitor and troubleshoot pipeline performance, failures, and operational issues. - Semantic & Analytics Enablement Support implementation of semantic models and business-friendly data structures for analytics and reporting. Collaborate with analytics and BI teams to improve consistency and usability of enterprise data assets. Assist in development of standardized metrics, dimensions, and reusable reporting datasets. Contribute to metadata and data catalog integration initiatives. - AI/ML Data Enablement Build and optimize AI-ready data pipelines supporting ML and GenAI initiatives. Support feature engineering and data preparation workflows for AI/ML use cases. Assist in integration with Vertex AI BigQuery ML Vector databases GenAI frameworks Contribute to implementation of semantic search and AI-assisted data interaction patterns. 6. Engineering Best Practices & Collaboration Follow established coding standards, architecture guidelines, and DevOps practices. Participate in code reviews, testing, debugging, and performance optimization activities. Collaborate effectively with architects, lead engineers, analysts, and client stakeholders. Contribute to engineering documentation, operational runbooks, and technical knowledge sharing. Continuously learn and adopt modern cloud, data engineering, and AI platform technologies. - Governance, Monitoring & Operational Support Support implementation of monitoring, logging, lineage, and observability frameworks. Ensure adherence to enterprise security, governance, and compliance standards. Assist in incident resolution, root cause analysis, and platform stability improvements. Contribute to continuous improvement initiatives for operational excellence and delivery quality. Technical Expertise Required Area Skills / Technologies Cloud Data Engineering GCP, BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage Data Processing SQL, Python, PySpark, DBT Streaming & Pipelines Apache Beam, batch & real-time processing Workflow Orchestration Cloud Composer (Airflow), Workflows Semantic & Analytics Basic semantic modeling concepts, reporting datasets, Looker exposure preferred AI/ML Enablement Vertex AI exposure, BigQuery ML, GenAI ecosystem awareness Metadata & .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.