Industry: Tech

Thinking about starting a career in data engineering? Here's a guide.

Introduction

Data. That's what guides every industry in making better decisions - from finance to gaming.

Data engineers make sure data is availabele, clean, and ready to use.

The demand for skilled data engineers boomed in the last 5 years. Why?

• The rise of AI-powered applications that require quality data pipelines

• The migration of companies to cloud-based analytics

• Increased focus on real-time data processing

Getting into data engineering is a smart career move:

• High Demand

• Remote-Friendly

• Career Growth Opportunities

• Lucrative Salaries (cloud/AI skills may grant higher pay)

Salary Expectations for Data Engineers in 2025

Approximate global ranges (USD):

• Entry-level: $65k – $95k

• Mid-level: $95k – $140k

• Senior: $140k – $180k+

Remote roles can sometimes pay more if you work for a U.S. or European company.

Here’s some good news:

You don’t need years of prior experience to get into the industry. 

With the right skills, portfolio, and strategy, you can land your first data engineering job within months—even if you’re switching careers.

What Exactly Do Data Engineers Do? (A Beginner-Friendly Breakdown)

A data engineer builds and maintains the systems that move, store, and organise data - the architects and builders of the data world.

Common tasks include:

• Designing ETL/ELT pipelines (Extract, Transform, Load).

• Managing data warehouses (Snowflake, BigQuery, Redshift).

• Handling streaming data (Kafka, Kinesis).

• Working with cloud platforms (AWS, Azure, GCP).

• Ensuring data quality, security, and compliance.

If you’re new, don’t worry — you’ll learn these tools and concepts gradually.

Can You Really Land a Data Engineering Job Without Experience?

Yes.

But let’s be realistic: companies rarely hire a complete novice with no proof of skills.

You may not need – or have – “job experience”, but you need “project experience”. Your portfolio matters more than your past job history; recruiters and hiring managers care more about your skills in, for example,

• Writing SQL queries

• Building simple data pipelines

• Working with cloud storage

• Understanding basic data modelling

So, get hands-on with it.

Skills You Need in 2025 – and How to Learn Them Fast

Here’s your essential starter tech stack:

Pro Tip: Focus on one cloud provider first (e.g., AWS or GCP) to avoid being overwhelmed.

For Foundational Skills:

For Core Data Engineering Skills

Bonus Skills to Stand Out in 2025

• Data Orchestration (Airflow, Prefect)

• Streaming Data (Kafka, Flink)

• Data Observability (Monte Carlo, Great Expectations)

Certifications and Learning Resources for Beginners

Certifications aren’t mandatory, but they signal credibility – especially without prior experience.

Recommended in 2025:

• Google Cloud Professional Data Engineer

• Snowflake SnowPro Core

• AWS Certified Data Engineer – Associate (new in 2025)

Free/Low-Cost Learning Platforms:

• Coursera (audit mode for free)

• DataCamp (free beginner tracks)

• YouTube channels like Seattle Data Guy, Data Engineering on Cloud

Build a Portfolio from Scratch (Even Without a Job)

A portfolio is your substitute for work experience.

Acquire real-world skills through:

Beginner Personal Projects (GitHub Portfolio)

1. Public Data Pipeline 

•  Ingest NYC taxi data → store in BigQuery → create a dashboard in Looker Studio.

• Build a real-time Twitter sentiment analysis pipeline (Python + Kafka + Spark).

1. Streaming Sensor Data 

•  Simulate IoT device data using Python → process with Kafka → store results in a PostgreSQL database.

• Set up a cloud data warehouse (AWS Redshift or Snowflake) and query it with SQL.

1. ETL with dbt 

•  Use dbt to transform raw sales data into cleaned, aggregated tables.

• Create an ETL pipeline that pulls data from an API, cleans it, and loads it into a database.

Freelance & Volunteer Work

• Upwork/Fiverr: Look for small ETL/data pipeline gigs.

• Nonprofits: Offer to help with their data infrastructure.

Open-Source Contributions

• Contribute to Apache Airflow, dbt, or other data tools.

• Fix bugs or write documentation (great for beginners).

Tips for Impact:

• Document each project in a GitHub repo with a README, diagrams, and screenshots. 

• Write a LinkedIn post or blog article about what you built.

Optimize Your Resume and LinkedIn for Data Engineering

Resume Tips:

• Use keywords from the job description: “ETL,” “Airflow,” “Snowflake,” “data pipelines.”

• Focus on projects. List them under your “Experience” section (yes, even self-initiated ones)

• Quantify results: “Built ETL pipeline that reduced processing time by 40%.”

• Use action verbs: “Built”, “Optimised”, “ Automated”, etc.

• Eg: Data Pipeline Project | Personal Project

  • Developed a Python ETL pipeline that reduced data processing time by 50%.

  • Deployed on AWS using Lambda and S3.

LinkedIn Tips:

• Headline: “Aspiring Data Engineer | Python • SQL • Airflow • Snowflake”

• Add portfolio projects under the “Featured” section.

• Post about your projects to attract recruiters.

• Request endorsements for technical skills.

Ace the Data Engineering Interview

Expect three types of interviews:

1. Technical Screening – SQL queries, Python coding challenges.

• SQL eg: “Write a query to find the top 5 customers by total spend.”

• Python eg: “How would you clean a dataset with missing values?”

1. System Design 

• Example question: “How would you design a pipeline to process daily sales data?”

• Answer by:

  • Breaking it into ingestion 🡪 storage 🡪 processing 🡪 analysis

  • Mentioning tools like Airflow, Spark, Snowflake

1. Behavioural – How you handle challenges, collaboration, and deadlines.

• STAR Method

• Eg: “Tell me about a time you solved a difficult problem.” 

  • Situation: what was the problem?

  • Task: what needed to be done?

  • Action: what did you do?

  • Result: what was the outcome?

Preparation Tips:

• Practice SQL daily (LeetCode, HackerRank).

• Review basic data modeling (star vs. snowflake schema).

• Be ready to explain your portfolio projects in detail.

Where to Find Entry-Level Data Engineering Jobs

Job Boards:

• LinkedIn Jobs

• Indeed

• Glassdoor

Entry-Friendly Companies:

• Startups (often flexible with formal experience).

• Consulting firms with data teams.

• SaaS companies with strong analytics needs.

Pro Tip: Search for “Junior Data Engineer,” “ETL Developer,” “Data Analyst (with ETL)” – sometimes entry-level roles are disguised under different titles.

Network Your Way into the Industry

Networking isn’t just for extroverts – think of it as relationship-building.

Ways to connect:

• Join Slack groups like DataTalks.Club and Locally Optimistic.

• Attend Meetup.com events for Python, data engineering, or cloud user groups.

• Engage on LinkedIn by commenting on posts from data engineers.

Direct Outreach Example:

“Hi [Name], I’m transitioning into data engineering and just completed a project with Airflow and BigQuery. I admire your work at [Company] and would love to hear how you got started.”

Future-Proofing Your Career

To grow beyond entry-level:

• Learn real-time streaming at scale (Kafka, Flink).

• Explore data mesh architectures.

• Stay updated on AI-assisted data pipeline tools.

• Mentor or write about your work to build credibility.

Your 90-Day Action Plan

If you’re starting from zero, here’s a 3-month roadmap:

Month 1:

• Learn Python & SQL basics.

• Build your first simple ETL pipeline.

Month 2:

• Learn cloud basics (AWS/GCP).

• Complete a portfolio project with Airflow + Snowflake/BigQuery.

Month 3:

• Polish resume & LinkedIn.

• Apply to 5–10 roles weekly + network with 2–3 people per week.

Final Note:

It is absolutely possible to land a data engineer job in 2025 without professional experience. 

Upskill yourself with affordable or free resources that are easily accessible. Utilise the skills you’ve learnt and build your portfolio: your passion projects are your proof of skills, showcase meaningful projects that demonstrate your abilities. 

Connect and network within the field, be prepared, and shine.

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