About Course
About Google Cloud Data Engineering
Bangalore's product and global capability centre density is the largest single driver of Google Cloud hiring in India. Product companies scaling on GCP run their build and release through Cloud Build, their services on GKE and Cloud Run, and their analytics in BigQuery, which means the whole delivery chain sits on one platform and rewards one connected skill set.
What makes Bangalore distinctive is how close cloud roles sit to product delivery. An architecture decision here is reviewed by the engineers who will operate it, so people who can move between design and implementation are hired faster and paid more than pure infrastructure specialists.
This Bangalore page carries the same 12-week, 72-hour Google Cloud Data Engineering syllabus, faculty and assessment as the master programme, delivered live online and in hybrid format, with classroom batches at our Thoraipakkam centre.
Course Overview
12-week Google Cloud Data Engineering training (72 hours) covering BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage and Dataplex. Professional Data Engineer prep.
Why Learn Google Cloud Data Engineering
Bangalore product teams deploy through Cloud Build onto GKE, so CI/CD plus container skills appear together in most cloud job descriptions here.
Global capability centres run their parent company's Google Cloud landing zone from Bangalore, which creates platform-engineering roles with group-level scope.
Fintechs scaling lending and payments need people who can apply VPC Service Controls and IAM boundaries, and they compete hard for that skill.
Google Cloud hiring sectors in Bangalore
- Product companies and SaaS exporters
- Global capability centres
- Fintech, lending and payments
- Consumer platforms and healthtech
Demand drivers behind Bangalore cloud hiring
- GKE and Cloud Run as the default deployment target for product teams
- BigQuery adoption for product analytics and experimentation
- GDPR and DPDP obligations on EU and Indian customer data
- Enterprise procurement demanding SOC 2 and ISO 27001 on cloud
Who Should Join This Course
- Data Engineers on AWS or Azure — Adding BigQuery and Dataflow to a multi-cloud profile
- Analysts and BI Developers — Moving upstream into pipeline and modelling work
- Software Engineers — Shifting from application code into data platform work
- Fresh Graduates — Targeting the largest data job family in Indian cloud
- Database Administrators — Transitioning from warehouses to lakehouse architectures
- Analytics Leaders — Needing to design and review the platform themselves
Prerequisites
No cloud experience required unless the course page states otherwise. Basic IT familiarity and comfort with a web console are enough to start; the platform, its vocabulary and its operating discipline are taught from first principles.
Learning Objectives
- Design batch and streaming pipelines with correct guarantees
- Build and optimise BigQuery warehouses and SQL
- Implement Dataflow pipelines with windowing and triggers
- Operate Dataproc and Spark against a Cloud Storage lakehouse
- Apply data quality, lineage and governance with Dataplex
- Orchestrate pipelines and ship them through CI/CD
- Tune cost: slots, partitioning, clustering and caching
- Sit the Professional Data Engineer exam with practice sets
Google Cloud Services and Tools Covered
BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Dataplex together with warehouse modelling practice, data-quality and lineage tooling, and the DPDP and RBI data-residency obligations that govern where pipelines may run.
Career Opportunities After This Programme
- Data Engineer (GCP)
- Analytics Engineer
- ETL and ELT Developer
- Data Platform Engineer
- Data Reliability Engineer
- BigQuery Administrator
Hands-on Practice and Capstone
Capstone: Build an end-to-end platform: capture streaming events into BigQuery with exactly-once handling, transform them in Dataflow with correct windowing, model the warehouse with partitioning and clustering, apply quality rules and publish lineage in Dataplex, orchestrate with SLAs and backfill support, then present the cost and reliability case.
Production scenario: A nightly pipeline silently delivered stale data for four days before anyone noticed. Rebuild it with freshness and completeness monitoring, a dead-letter path for malformed records, and an alert that fires before the business does.
Course Duration, Mode and Certification
The course runs for 12 weeks (72 hours), three sessions a week of two hours each. Modes: Classroom / Live Online / Hybrid. Certifications mapped: Professional Data Engineer, SkilBrill Course Completion Certificate.
Is classroom training available in Bangalore? Classroom batches run at our Thoraipakkam centre in Chennai. Learners in Bangalore join the same live online or hybrid cohort taught by the same faculty, with the same assessment and placement support.
Why Train at SkilBrill
SkilBrill Training Institute in Thoraipakkam focuses on making you employable. Trainers are working cloud engineers and architects, every module produces a portfolio artefact rather than a completion badge, and the placement cell connects eligible candidates with hiring companies. Fee: ₹45,000 – ₹75,000. Enquire on +91 8610964691.
Career and Job Support
- Resume assistance tailored to cloud and platform roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering architecture, operations and troubleshooting scenarios
- Capstone project walkthrough preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹6 – 14 LPA
Frequently Asked Questions
What is Google Cloud Data Engineering training?
It is the programme for building and operating data platforms on Google Cloud: ingestion, batch and streaming transformation, BigQuery warehousing, governance and operations. SkilBrill runs it over 12 weeks and 72 hours with the Professional Data Engineer exam as the spine.
What does the Google Cloud Data Engineering syllabus cover?
Eight modules: the data platform design, ingestion batch and streaming, Dataflow pipelines, BigQuery warehousing and modelling, Spark and Dataproc on a lakehouse, data quality and governance with Dataplex, orchestration and CI/CD, and an end-to-end platform capstone.
What is the duration and fee of the Google Cloud Data Engineering course?
12 weeks, three sessions a week of two hours each, totalling 72 classroom hours. Fees are ₹45,000 – ₹75,000 depending on batch and mode. Weekday, weekend and evening batches are open.
Which certification does this course prepare me for?
Professional Data Engineer, plus SkilBrill’s own course completion certificate. Exam registration and fees are paid directly to Google Cloud and are separate from the course fee.
Do I need SQL or coding experience for this course?
Strong SQL is assumed and is the single most useful preparation. Python is used in the Dataflow modules and is taught in the context of the pipelines rather than from first principles. Analysts with solid SQL typically do very well.
What jobs can I get after Google Cloud Data Engineering?
Data Engineer on GCP, Analytics Engineer, ETL and ELT Developer and Data Platform Engineer. Freshers in India typically start at ₹6–14 LPA, with BigQuery-heavy and real-time roles at the higher end.
Is classroom training available in Bangalore?
No. SkilBrill's classroom runs only in Chennai. Learners in Bangalore join the same live online / hybrid cohort taught by the Chennai faculty, with proctored labs and the same placement support.
Which companies hire for these skills in Bangalore?
Learners in Bangalore typically target a mix of MNCs and startups such as Infosys, Wipro, Accenture, IBM, Microsoft. The programme's placement support applies equally to Bangalore learners.
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