DataUptoPakistan Careers

Free roadmap · Up to 6 months · 2 phases · 8 stages

Data Engineer roadmap

Learn how to build data pipelines, manage databases, transform information, and develop the foundations of modern data infrastructure.

Who it's for

  • • Developers moving into data
  • • Analysts who enjoy automation
  • • IT and software engineering students

Prerequisites

  • • Comfort with a computer and file systems
  • • Basic programming helps
  • • 15–20 hours of study per week

Tools you'll use

PythonSQLPostgreSQLAirflowdbtSparkCloud

Phase 1 — Foundations

Build the base: Python and Linux, advanced SQL, and the data modelling that every pipeline depends on.

Phase 2 — Intermediate & Advanced

Level up: ETL/ELT pipelines, orchestration with Airflow and dbt, cloud data platforms and big-data fundamentals.

Cloud platforms, distributed systems and production-grade engineering can require substantial additional practice.

Where this career takes you

The designations employers hire for, the cities with the strongest demand, and the duties you'll own day to day.

Job roles in the market

  • • Data Engineer
  • • Analytics Engineer
  • • ETL Developer
  • • BI Developer
  • • Platform Engineer (data)
  • • Database Developer

High-demand cities

  • • Karachi
  • • Lahore
  • • Islamabad
  • • Remote (worldwide)

Core duties

  • • Design databases and data warehouses
  • • Build and monitor data pipelines
  • • Model data for analytics teams
  • • Automate data quality checks
  • • Deploy data platforms on cloud

Learning plan

PHASE 1

Foundations

Build the base: Python and Linux, advanced SQL, and the data modelling that every pipeline depends on.

  1. 1

    Topics — tick as you learn

    Practice exercises

    • → Script that downloads API data to CSV daily
  2. 2
  3. 3

PHASE 2

Intermediate & Advanced

Level up: ETL/ELT pipelines, orchestration with Airflow and dbt, cloud data platforms and big-data fundamentals.

  1. 4
  2. 5
  3. 6
  4. 7
  5. 8

External resources are recommended for your learning journey and are not produced or endorsed by DataUpto.

Portfolio projects

Beginner

API-to-database pipeline

Pull public data on a schedule and load it safely into PostgreSQL.

Intermediate

Scheduled warehouse pipeline

Airflow + dbt building a star schema with tests.

Intermediate

Data quality monitor

Automated checks that alert when data breaks.

Advanced

Cloud data platform

An end-to-end lakehouse demo on one cloud provider.

Find datasets for these projects →

Common mistakes to avoid

  • Trying to learn every cloud at once
  • Skipping SQL depth for trendy tools
  • Pipelines that aren't idempotent
  • No logging or monitoring
  • No architecture diagram in the portfolio

Final self-assessment

Before applying for entry-level data engineer roles, you should be able to:

Interview preparation

SQL & PythonDatabase designETL vs ELTData warehousingPipeline reliabilityScenario-based architecture
Practise questions

Suggested next steps

  1. 01

    Practise data engineering questions

  2. 02

    Explore datasets with APIs

  3. 03

    Explore the Data Engineer Program

Build resumeView Data Engineer Program