Design pipelines, warehouses and streams that are tested, governed and cost-aware — the foundation every analytics and AI team needs.
Teams want dashboards, models and copilots, but the data arrives late, duplicated or untrusted. Platform engineers make data dependable: modelled, monitored and fast enough for the business.
Every week has a hands-on lab and an assignment. Open a week to see both.
Model and move data correctly
Pull daily FX rates from an API and land them as files.
Add retries, structured logging and tests to the loader.
Rewrite a slow settlement report with window functions.
Read the query plan and document what made it faster.
Design a star schema for card payments.
Model merchant changes as a type 2 dimension.
Build an incremental load for card transactions.
Prove the load is idempotent by re-running it safely.
Transform, orchestrate and scale
Build staging and mart models for payments in dbt.
Add tests and publish dbt docs for every model.
Orchestrate extract, load and dbt runs in one DAG.
Alert on failures and missed SLAs.
Organise raw, cleaned and curated layers in a lakehouse.
Partition the largest table and measure the query speed-up.
Aggregate a year of transactions with Spark on Databricks.
Fix a skewed join and report the before and after.
Stream, govern and operate
Stream card authorisations through Kafka.
Emit velocity-based fraud signals within seconds.
Add Great Expectations checks to your pipeline.
Write a data contract and block loads that break it.
Mask card numbers and lock down access by role.
Produce a cost report with three savings you made.
Agree scope and milestones for your capstone platform.
Deliver the platform, runbook and stakeholder demo.
The fast track covers the core in 12 weeks. The standard pace spreads the same content and the capstone over up to 16 weeks.
Plan for 8–10 hrs/week, including the live session.
Incremental ingestion of card transactions into a tested, documented warehouse.
Card transactions, merchants and FX rates
A Kafka stream that enriches transactions and emits velocity-based fraud signals in seconds.
Streaming card authorisations
select d.merchant_id, sum(f.amount_inr) as gmv, count_if(f.is_refund) as refunds from {{ ref('fct_payments') }} f join {{ ref('dim_merchant') }} d
A dimensional model with marts for finance, risk and growth teams.
Payments, refunds and customer accounts
Build a production-style platform that ingests batch and streaming payments data, models it into trusted marts and serves features to a fraud model and an analytics dashboard.
Dataset: Authorisations, settlements, refunds and chargebacks across 12 months
Prefer another domain? Choose SaaS, E-commerce, Logistics or Healthcare at the capstone kick-off.
Build and run pipelines and warehouses
Own modelling, testing and semantic layers
Operate infrastructure for data and AI teams
These are the roles this track prepares you for. AI4Impact provides career support; it does not guarantee employment, interviews or a particular salary.
Completion records that you did the work. The professional certificate is awarded only after an independently reviewed capstone and a recorded defence, so it means something to the people hiring.
Add it to any track. Five modules that take you from a finished capstone to real interviews, for ₹24,999.
Each week has short recorded lessons, a hands-on lab and an assignment, plus a live clarification session with your trainer. Plan for 8–10 hours a week, including the live session.
They are part of Programme+: five one-to-one 30-minute sessions you book through your learner dashboard and use for scoping, reviews or career direction.
Fintech is the default. If you prefer another domain, you can choose SaaS, E-commerce, Logistics or Healthcare at the capstone kick-off.
No. AI4Impact provides career support and recommendations, but it does not guarantee employment, interviews or a particular salary. Hiring decisions are made by employers.
Book a free 20-minute call. We look at your background and tell you honestly which track fits, or if none of them do yet.
