Intake open · 3-month track

Big Data Analytics Internship

When the dataset stops fitting in pandas, everything changes. You will build the pipelines, the warehouse and the scheduling that analytics teams depend on.

Apply now See the 12-week plan
3Months
HybridFormat
389+Enrolled
FreeTo apply
Pipeline. Nightly
# fit and validate
model.fit(X_train, y_train)
score = evaluate(model, X_val)
Rows processed48.2 M
epoch 1epoch 10
Scale
Beyond one laptop
Mentor review
Weekly, 1-to-1
  • Spark
  • Airflow
  • SQL
  • Data Warehouse
  • Python
  • dbt
  • Spark
  • Airflow
  • SQL
  • Data Warehouse
  • Python
  • dbt
  • Spark
  • Airflow
  • SQL
  • Data Warehouse
  • Python
  • dbt
  • Spark
  • Airflow
  • SQL
  • Data Warehouse
  • Python
  • dbt
  • Spark
  • Airflow
  • SQL
  • Data Warehouse
  • Python
  • dbt
  • Spark
  • Airflow
  • SQL
  • Data Warehouse
  • Python
  • dbt
  • Spark
  • Airflow
  • SQL
  • Data Warehouse
  • Python
  • dbt
  • Spark
  • Airflow
  • SQL
  • Data Warehouse
  • Python
  • dbt
12Weeks
25 hrsPer week
389+Interns trained
1Data pipeline
The work

Pipelines fail at 3am. Yours should tell you why

Data engineering is judged on what happens when things break: a schema changes, a source goes down, a job runs twice. You will build for that from week one.

  • 1You map the sources and agree the warehouse model
  • 2You build extraction and transformation as code
  • 3Your mentor reviews idempotency and failure handling
  • 4You schedule it, monitor it and document the lineage

Weeks 1 to 3 cover Python and SQL from zero. No prior data engineering needed.

dags/daily_sales.py
# Week 8: idempotent, backfillable, alerting on failure
with DAG("daily_sales", schedule="0 2 * * *",
         start_date=days_ago(30), catchup=True,
         default_args={"retries": 2, "on_failure_callback": alert}) as dag:

    extract = PythonOperator(
        task_id="extract", python_callable=pull_orders,
        op_kwargs={"run_date": "{{ ds }}"})       # partition per day

    transform = SparkSubmitOperator(
        task_id="transform", application="jobs/clean_orders.py",
        conf={"spark.sql.shuffle.partitions": "64"})

    load = PythonOperator(task_id="load", python_callable=upsert_fact)

    extract >> transform >> load
Skills

Eight things you will actually be able to do

01

Python for pipelines

Writing extraction and transformation code that is testable and rerunnable.

02

Advanced SQL

Window functions, CTEs, query plans and making a slow query fast.

03

Data modelling

Star schemas, facts, dimensions and designing a warehouse analysts can use.

04

Apache Spark

Distributed processing, partitions, shuffles and why your job is slow.

05

Orchestration

Airflow DAGs, dependencies, retries, backfills and idempotent tasks.

06

Data quality

Validation tests, freshness checks and catching bad data before analysts do.

07

Warehousing

Loading strategies, incremental models with dbt, and partitioning for cost.

08

Serving analytics

Exposing clean marts to dashboards and keeping them fast.

Curriculum

The 12-week plan

Four phases. Each one ends in a deliverable that is reviewed and graded before the next opens.

Foundations

Python and SQL at data-engineering depth.

  • Python, environments and file formats: CSV, Parquet
  • SQL joins, aggregates, window functions, CTEs
  • Query plans, indexes and reading EXPLAIN output
  • Git and code review for data projects
Deliverable

A repeatable extraction script pulling from two sources into partitioned Parquet files.

The capstone

Every intern ships a pipeline that runs nightly

You own an Ezitech data pipeline end to end (sources, transformations, warehouse model, schedule and monitoring), feeding dashboards the team actually reads.

  • A modelled warehouse with tested transformations
  • A scheduled pipeline that backfills and recovers
  • Monitoring and alerting you configured yourself
Start your application
Ezitech seminar session with students at a university auditorium
Ezitech stall at a university open house and job fair
Ezitech receiving a shield at a university project presentation
Ezitech team and interns group photo outside the institute
Ezitech speaker presenting at a talent award ceremony
Ezitech mentors reviewing intern project documents
Ezitech team discussing an intern project brief in the office
01 / 07
Duration12 weeks
Commitment25 hrs / week
FormatOnsite + remote
LocationRawalpindi, PK
LevelJunior / fresher
ReviewsWeekly, 1-to-1
The Ezitech model

What every Ezitech intern gets

Three things that make this different from a course you watch on a screen.

Live client briefs

You work on real Ezitech projects with real deadlines and real revision rounds, not recycled practice exercises.

A mentor, not a playlist

Weekly reviews with someone who does this work for a living, plus a team of interns around you to build with.

Portfolio and experience letter

You finish with shipped work you are allowed to show and a letter that says exactly what you delivered.

And you leave with

  • Verified certificate

    Issued by Ezitech Institute and verifiable by employers.

  • Experience letter

    On company letterhead, stating exactly what you delivered.

  • A running pipeline

    A scheduled ETL pipeline feeding a modelled warehouse.

  • Interview preparation

    CV rewrite, portfolio case study and a technical mock interview.

  • A route into the team

    Top performers are considered first for paid junior roles.

  • Confidence in review

    Twelve weeks of defending your own work to a senior practitioner.

Built for you if

  • You are an undergraduate or fresh graduate in CS, IT or SE
  • You are comfortable with Python and interested in SQL
  • You prefer building systems to building models
  • You want data engineering roles, which hire heavily

Not a fit if

  • You want a certificate without doing the work
  • You cannot give 25 hours a week for 12 weeks
  • You are looking for a purely theoretical course
  • You want to skip review and work alone
FAQ

Questions people ask

Everything applicants email us about, answered before you apply.

Still not sure?

Talk to the admissions team before you commit twelve weeks.

Contact Ezitech
How is this different from the Data Science track?
Data science answers questions with data. This track builds the infrastructure that makes those questions answerable: pipelines, warehouses, scheduling and quality checks. Data engineering roles are also currently easier to get hired into as a fresher.
How many hours a week does it take?
A minimum of 25 hours across 12 weeks, split between onsite sessions, mentor reviews and independent work. Interns who treat it as full-time finish with noticeably stronger portfolios.
Is it remote or onsite?
Hybrid. Onsite days run at the Ezitech office in Rawalpindi; the rest of the week is remote with scheduled check-ins. Candidates outside Rawalpindi and Islamabad can complete the track fully remotely if they attend the weekly review calls.
Do I need a cluster to learn Spark?
No. You start with Spark running locally, which teaches the same concepts, and move to a managed cluster for the capstone using Ezitech’s environment during the final phase.
Can final-year students apply?
Yes, and many do. The capstone is regularly used as a final-year project because it is built from a real brief with real requirements. Agree the scope with your mentor in week one if your university has a set submission format.
How do I apply?
Register on the Ezitech intern portal, choose the Big Data track, and complete the three-step form with your education details and CV. Shortlisted candidates get a short screening call before the intake starts.
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Applications open

Build the pipeline analysts depend on

Three months, one shipped piece of real work, a mentor who reviews you every week. Registration takes five minutes.

Apply for this internship Talk to us first
Ezitech Institute · Rawalpindi, Pakistan · info@ezitech.org · +92 345 5555396