Private corporate batches, live online cohorts and 1-on-1 mentoring in measurement that stands up to scrutiny — GA4 event design, tag management, attribution, explorations and BigQuery export — taught by a practitioner who runs it in production.
20 years across DevOps, SRE and Security · 10,000+ engineers trained · Trained teams at JPMorgan Chase, Verizon, Nokia and the World Bank
Principal DevOps Engineer & Architect
Rajesh teaches Google Analytics as a measurement engineering problem rather than a report tour: how data is actually collected, what an event and its parameters become inside the GA4 model, and why the schema you design at implementation time is the ceiling on every question you can later ask. Sessions work through the full path — account, property and data stream structure, tagging with Google Tag Manager including the data layer, triggers, variables and versioned publishing, event and parameter naming conventions, registering custom dimensions and metrics, key events and conversion definitions, and cross-domain and subdomain measurement done properly. The analysis half covers standard reports, explorations with funnel, path and segment overlap techniques, audiences, UTM campaign tagging discipline, attribution models, and the BigQuery export for when sampling and cardinality limits make the interface insufficient — alongside consent, data quality auditing, filters and the governance that keeps a property trustworthy.
Twenty years across DevOps, SRE and Security, in principal and architect roles at PayPay, SoftwareAG, ServiceNow, JDA Software, Intuit, Adobe and others. He has trained engineers at JPMorgan Chase, Verizon, Nokia, the World Bank, VMware, Oracle, Mercedes-Benz and Airbus — more than 10,000 people personally. He teaches what he runs, not what he reads.
You are booked with a named engineer, and that is who turns up. Marketplaces and larger providers rotate whoever is free, so the person who sold you the agenda is rarely the person teaching it.
The same trainer is available for the next engagement, which matters when a team builds on what it learned last time.
Your batch is assigned a named trainer before it starts, and that is who teaches it. See the full faculty.
Engagements are matched on the tool, not the calendar. For Google Analytics that means a trainer who has run it in production — measurement that stands up to scrutiny — GA4 event design, tag management, attribution, explorations and BigQuery export — rather than whoever is free that week. You are told who is teaching before you commit, and that person is on the discovery call that shapes the agenda.
Where a batch is large enough to need a second trainer, the pairing is declared up front. The lead trainer stays accountable for the syllabus and the assessment either way.
Principal DevOps Engineer & Architect
Twenty years across DevOps, SRE and Security in principal and architect roles at PayPay, SoftwareAG, ServiceNow, JDA Software, Intuit, Adobe, IBM/Emptoris, Ness, MindTree and Accenture. He has trained more than 10,000 engineers personally, at organisations including JPMorgan Chase, Verizon, Nokia, the World Bank, VMware, Oracle, Mercedes-Benz and Airbus. He teaches what he runs, not what he reads.
Teams of 8–30
Custom agenda, your timezone, onsite or online, NDA-friendly.
Request a quoteIndividual engineers
A private instructor and a curriculum built around your goal.
₹99,999Individuals who want peers
Scheduled batch, max 8 to 10 hours of live instruction.
₹34,999A private batch starts with a discovery call. We look at the stack you actually run — the CI system, the cloud, the constraints — and map the agenda onto it, so examples use your topology rather than a generic one.
Delivery is onsite at your premises, live online, or hybrid, scheduled around your release calendar rather than ours. Batches run 8 to 30 engineers.
Every attendee leaves with recordings, slides, lab repositories and a completion certificate. You receive an attendance and assessment report. Invoicing supports PO and GST.
Talk to us about a private Google Analytics batchGoogle Analytics is a digital analytics platform that collects behavioural data from websites and applications, processes it into reports, and lets you ask questions about how people found a product and what they did once they arrived. It is the most widely deployed measurement tool on the web, which means the questions it answers — where traffic comes from, which journeys convert, where users leave — have effectively become the shared vocabulary of digital teams.
The current version, GA4, is a different product from the Universal Analytics it replaced, and the difference is structural rather than cosmetic. Universal Analytics modelled the world as sessions containing pageviews, with events bolted on and a rigid category, action and label schema. GA4 models everything as an event with parameters, applies the same model to web and app data streams, and reconstructs sessions afterwards. That single change is why almost every reporting habit, goal definition and custom dimension had to be rebuilt during the migration, and why so many properties still carry configurations that were translated mechanically rather than redesigned.
Around the core collection sit the parts that make Google Analytics usable at a professional level. Google Tag Manager, which is where most implementations actually live, controls what is sent and when. The data model of dimensions, metrics, custom definitions and audiences determines what can be reported at all. Explorations provide free-form funnel, path and segment analysis beyond the standard reports. The BigQuery export removes the sampling and cardinality ceilings entirely. Consent Mode and the data retention and privacy controls determine whether any of it is lawful in your market.
Every organisation with a digital product now has to justify spend against measured behaviour, and Google Analytics is where that argument usually gets made. Marketing budgets, product roadmaps and conversion targets are all defended with reports from this tool, which makes the person who understands its data model unusually influential and the person who does not unusually exposed.
The timing matters because the GA4 transition left a large amount of unfinished work. Universal Analytics stopped processing data, and many properties were migrated by mechanically recreating old goals as new conversion events without redesigning the event schema underneath. The result is properties that report numbers nobody trusts: duplicated events, missing parameters, unregistered custom dimensions, cross-domain journeys broken into two users, and internal traffic counted as acquisition. Cleaning that up requires someone who understands both the old model and the new one.
The third driver is regulation and measurement quality generally. Consent requirements, cookie restrictions, ad blockers and cross-device journeys all degrade naive client-side collection. Consent Mode, server-side tagging, the Measurement Protocol, modelled conversions and the BigQuery export are the professional responses, and they are engineering work rather than interface configuration.
Why measurement exists and what it can honestly tell you. The measurement lifecycle from collection through configuration and processing to reporting, what happens inside each stage, and the structural break between Universal Analytics and GA4 — because most confusion in existing properties traces back to a habit carried over from the old model.
Topics: Why digital analytics, and the questions it can and cannot answer · How Google Analytics works: collection, configuration, processing, reporting · The Universal Analytics session model versus the GA4 event model · What changed in the migration and what broke · Users, sessions, events, parameters and engagement · Dimensions and metrics, and the scope each has · Sampling, thresholding, cardinality and data freshness · Google Analytics against Adobe Analytics, Matomo, Plausible and product analytics tools · Where Google Analytics is the wrong instrument
Getting the container right before any data flows. Organisations, accounts, properties and data streams; how to organise a multi-site, multi-app or multi-region estate; the settings that cannot be changed later; and the data model itself — automatically collected events, enhanced measurement, recommended events and custom events.
Topics: Google Analytics setup: account, property and data stream creation · Organising an Analytics account across sites, apps and regions · Data streams for web, Android and iOS, and how they combine · Property settings that are irreversible · Automatically collected events and what they cover · Enhanced measurement: scrolls, outbound clicks, site search, video, file downloads · Recommended events and why the naming matters · Custom events and parameters · Reporting identity: user ID, Google signals, device ID and modelling · Data retention settings and their reporting consequences · Internal traffic and developer traffic filters · Time zones, currency and their effect on reporting
Where real implementations live. The tag management model and why hard-coded tags become unmaintainable; the data layer as the contract between developers and analysts; tags, triggers and variables; preview and debug mode; versioning, workspaces and publishing; then the harder cases — single-page applications, cross-domain measurement and server-side tagging.
Topics: Overview of Google Tag Manager and why it exists · Containers, workspaces, versions and the publishing workflow · The data layer as a contract with the development team · Tags: GA4 configuration and event tags · Triggers: page view, click, form, scroll, timer, custom event · Variables: built-in, data layer, custom JavaScript, lookup tables · Preview and debug mode, and reading the tag assistant · Single-page applications and history change triggers · Sub-domain tracking and shared cookie scope · Cross-domain tracking and linker configuration · Consent Mode and consent-aware tag firing · Server-side tagging and the Measurement Protocol · Naming conventions and container governance
The schema decisions that set the ceiling on everything reportable. How to design an event taxonomy that scales, parameter naming and typing, registering custom dimensions and metrics and the quotas that apply, calculated metrics, content grouping, and the item-scoped parameters that ecommerce measurement depends on.
Topics: Designing an event taxonomy for a product · Event and parameter naming conventions · Understanding user behaviour with event tracking · Event-scoped, user-scoped and item-scoped parameters · Creating your own custom dimensions · Creating your own custom metrics · Registration quotas and what happens when you exceed them · Calculated metrics · Content grouping · Ecommerce events and the items array · User properties and when to use them instead of parameters · Importing offline and CRM data · Avoiding duplicate and overlapping events
Reading what has been collected. Navigating the report structure, overview versus detail reports, the life-cycle and user report collections, realtime and DebugView, comparisons and secondary dimensions, the reporting library and customising collections, and sharing — scheduled exports, shortcuts and the limits of a dashboard.
Topics: Navigating Google Analytics and the report structure · Understanding overview reports and full detail reports · Realtime reports and DebugView · Acquisition reports: traffic acquisition and user acquisition · Engagement reports: pages, screens, events, landing pages · Monetisation and retention reports · User and demographic reports · Tech reports: platform, device, browser and app version · Comparisons, filters and secondary dimensions · The reporting library: editing collections and detail reports · Setting up dashboards, shortcuts and bookmarks · How to share reports and schedule exports · Reading data quality signals: not set, other, and thresholded rows
Where visitors come from, and why that answer is so often wrong. Channel groupings and how sources are classified; direct, referral, organic and paid; UTM campaign tagging as a discipline with a convention rather than an improvisation; the causes of fractured campaign reporting; Google Ads and Search Console linking; and attribution models with their honest limitations.
Topics: Where visitors come from: source, medium, campaign and channel · Direct, referring sites, organic search and paid traffic · Default channel groupings and custom channel groups · Tagging campaigns for email, social media, offline and print · Building URLs with the campaign URL builder · Structuring incoming traffic tags as a convention · How to avoid fractured campaign reports · Fixing traffic sources that report unhelpfully · Referral exclusions and self-referral problems · Payment gateways and third-party redirects breaking attribution · Linking Google Ads and finding Ads reports in Analytics · Linking Search Console and what it adds · Attribution models, lookback windows and data-driven attribution · Measuring social media and offline campaigns
Turning behaviour into business outcomes. The milestones a user passes on the way to becoming a customer and how to measure each; key events and conversion configuration; ecommerce and purchase measurement; funnel construction and where users actually leave; and counting methods, which quietly change every number in a report.
Topics: The milestones in a customer journey and what each needs measured · Marking events as key events and conversions · Conversion counting methods and their effect on totals · Using goals and objectives to measure business outcomes · Ecommerce measurement: view, add to cart, checkout, purchase · Revenue, refunds and transaction integrity · Building funnel explorations and reading drop-off · Open and closed funnels, and when each is appropriate · Measuring Google Ads campaigns and conversion import · Cross-device and modelled conversions · Conversion value and assigning it honestly · Reconciling Analytics numbers with a back-end system
Understanding who, not just how many. What a user actually is in Google Analytics and the identity spaces that define it; demographics, geography and language; frequency, recency and engagement; building audiences with conditions and sequences; publishing audiences to Google Ads for remarketing; and the difference between an audience and a segment.
Topics: What a user is in Google Analytics, and how identity is resolved · Geography, language and how they are inferred · Demographics and interests, and their coverage limits · New versus returning users, frequency and recency · Engagement rate, engaged sessions and average engagement time · Building audiences with conditions and sequences · Audience triggers and membership duration · Predictive audiences and their prerequisites · Publishing audiences to Google Ads for remarketing · Segments in explorations versus audiences in a property · Comparisons for slicing standard reports · Cohort analysis and retention
Analysis beyond the standard interface. The exploration techniques — free form, funnel, path, segment overlap, cohort, user lifetime — and what each is genuinely for; building custom reports and detail reports; then the BigQuery export, which removes sampling, thresholding and cardinality limits and turns the raw event stream into something you can query with SQL.
Topics: Dimensions and metrics available for exploration · Free-form exploration: rows, columns, segments and cell types · Building your own custom funnels · Path exploration and where journeys actually go · Segment overlap and unified segments · Cohort exploration and user lifetime · Building your own custom reports and detail reports · Sampling in explorations and how to see it · Looker Studio connections and dashboard building · The Google Analytics Data API for programmatic reporting · The BigQuery export: schema, event tables and nested fields · Querying raw events with SQL: sessions, funnels and retention · When to leave the interface entirely
Making the numbers trustworthy and lawful, and connecting them to decisions. Auditing a property end to end; filters, internal traffic and bot exclusion; consent requirements and Consent Mode; retention and deletion; access control; then the strategic layer — choosing what to measure, annotating changes, alerting on anomalies, and communicating results to people who will act on them.
Topics: Auditing a property: collection, configuration and reporting checks · Setting up advanced filters on data · Internal traffic, developer traffic and bot filtering · Spam and referral pollution · Data deletion requests and retention controls · Consent requirements, Consent Mode and modelled data · Regional privacy considerations and IP handling · Access management, roles and data restrictions · Alerts, anomaly detection and annotations · Introduction to measurement strategy: from question to metric · Communicating data with exports, dashboards and narrative · Building a measurement governance process that survives staff turnover · Course review and where to go next
We rebuild the agenda around the tools you actually run.
Request a custom agendaBuild a data layer specification, implement six events through Google Tag Manager with triggers and variables, and verify every one in preview mode and DebugView.
Produce a full event and parameter taxonomy for a real product area, register the custom definitions, and prove each one appears correctly in reporting.
Diagnose a property with self-referrals, an untagged email campaign and a payment gateway breaking attribution, then fix all three and prove the correction.
Instrument a full purchase funnel, build an open and a closed funnel exploration over it, and reconcile the conversion count against a back-end total.
Answer one analytical question in an exploration and again in BigQuery SQL over the raw export, then explain every discrepancy between the two results.
Audit, remediate and document a full property — structure, event schema, tagging, conversions, consent, access and reporting — and hand over a governance document.
Billed yearly at ₹9,996
Max 8 to 10 hours of live instruction
Full program, private instructor
8–30 engineers · custom agenda · onsite or online · PO and GST invoicing
Refunds. If we cancel or postpone a cohort, you get a full refund within 15 days. There is no money-back guarantee otherwise.
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Certificate of completion
My experience with the AIOps training was positive. The course covered important topics in a structured way, and Rajesh Kumar explained the concepts patiently. I found the practical aspects particularly helpful because they made the technical content easier to understand.
I was looking to improve my understanding of AIOps, and this training helped me achieve that goal. Rajesh Kumar explained the subject in a structured and practical manner. The sessions on different AIOps concepts were informative.
I recently did a SRE Session with Rajesh Kumar from DevOps School and the session was great. Right from 1st day till day 15, we had a very interactive session. Rajesh clarified our doubts and the tool demos were excellent without any hiccups. He simplified the concepts while sticking to the content with a fine balance between theory and practice. Am convinced he is one of the best trainers for SRE & DevOps concepts.
The Rundeck developer session was excellent and highly engaging. I appreciated how well the session was structured, with the theoretical concepts explained clearly and in simple terms. What stood out most to me was the demo — it was both informative and enjoyable. I especially liked how Rajesh walked us through not only the happy path but also the sad path, showcasing common issues and sharing practical troubleshooting tips.
Rajesh's experience and knowledge are exceptional and we learnt invaluable practical knowledge which we can apply in our production environment. Incredibly friendly and gave us a fantastic insight both in-depth and at a high level of the Rundeck product.
Great learning experience from a very knowledgeable instructor with well-prepared course notes. The lab exercises on AWS instance work well to learn the hands-on side of the course.
| What matters | YouTube + blogs | Generic online course | Freelance marketplace | DevOpsSchool |
|---|---|---|---|---|
| Named practitioner | No | Rarely | Varies per booking | Yes — same trainer each time |
| Production experience | Unknown | Unknown | Unverified | 20 years, named employers |
| Custom agenda | No | No | Sometimes | Built from your stack |
| Onsite delivery | No | No | Sometimes | Yes |
| Lab environment | None | Sandbox that expires | Varies | Your own cloud — skill goes with you |
| Assessment | None | Quiz | Rarely | Assignments + capstone per module |
| Per-attendee certificates | No | Sometimes | Rarely | Yes |
| Corporate invoicing | No | Limited | Varies | PO and GST |
| Post-training support | None | Forum, time-limited | None | Lifetime forum access |
Tell us the team, the stack and the timeline. You'll get a straight answer, not a sales sequence.
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