How to Go from New Grad to Data Engineer

Use this roadmap to build your first DE portfolio & land your first offer — without a CS degree or 5 YOE. Designed by Avantikka Penumarty (Ex-META | Snr. Data Engineer)

👋 Welcome!

Hi there,

You’re here because you want more than just theory. You want a roadmap that actually works.

This isn’t fluff. I built this guide based on what I wish someone gave me when I was starting out — real skills, real proof, and real steps that lead to interviews and offers. No gatekeeping. No unnecessary jargon.

I’m not here to sell you a bootcamp. I’m here to show you that you can build production-level data systems — even without a CS degree, fancy title, or 5+ years of experience.

If you're serious about making the jump into DE, everything you need to start is in this email.

Let’s begin.

---

If you're a new grad trying to break into Data Engineering, you're not alone.

Data Engineering is one of the fastest-growing roles in tech today — but also one of the most misunderstood. Most beginners assume they just need to learn SQL and Python. But here's the hard truth:

Knowing SELECT * FROM table_name is not enough.

Thousands of grads take bootcamps, binge YouTube tutorials, and still never land interviews. Why? Because they have no real-world proof that they can build or maintain data systems.

And in this field, that proof matters more than any degree or certificate.

So let’s fix that.

---

Why Data Engineering?

It’s one of the most practical, future-proof, and underrated roles in tech today.

Perfect for those who:

• Enjoy backend more than frontend

• Love building real systems over tweaking models

• Want high pay, low ego, and fewer LeetCode puzzles

But DE is also one of the most misunderstood roles.

New grads think they just need SQL + Python.
Others spend months collecting certifications.
But neither gets interviews.

Why? Because they’re missing the one thing that matters most:

Proof that you can build and run data systems.

That proof > any degree, certificate, or keyword-stuffed resume.

---

What Do Data Engineers Actually Do at Work?

This is what you'll be expected to do in a real DE role:

• Build pipelines that ingest, clean, and transform raw data

• Maintain data quality, integrity, and freshness

• Work with analysts, ML teams, and backend engineers

• Manage cloud resources (like S3, Snowflake, BigQuery)

• Monitor systems, set up alerts, and debug failures

• Optimize queries, improve performance, and own uptime

Think of yourself as the plumber of the data world. You build and maintain the pipes that make everything else possible.

---

Here’s What You Actually Need to Land Your First DE Role:

✅ Go beyond basic SQL.
Learn how to use CTEs, window functions, GROUPING SETS, and indexes. Hiring managers want to see that you can write performant queries, not just run simple reports.

1. Build ETL pipelines.
   Get hands-on with Airflow and dbt. Learn to extract messy data, clean it up, and load it into warehouses. Use cloud tools like AWS S3 or GCP BigQuery to simulate real infrastructure.

2. Understand data modeling.
   Study how companies design scalable data systems. Know what a Star Schema is. Learn dimensional modeling. Explore how tools like Snowflake and Redshift structure data for analytics at scale.

3. Publish real projects.
   Build 1–2 end-to-end data pipelines using public datasets. Document them clearly on GitHub. A great portfolio can often do more than a great resume — especially when you're just starting out.

And what you don’t need:

❌ A CS degree
❌ 5+ years of experience
❌ Perfect code

You just need to prove that you understand how data flows through a system — and that you can make it work in production, not just in a classroom.

---

New Grad to Data Engineer: The Real Roadmap

---

Month 1 – Build Your Foundations (SQL + Python)

This month is NOT just about “learning basics.” It’s about mastering fundamentals that you’ll actually use in interviews and pipelines.

SQL:

• Learn SELECT, WHERE, GROUP BY, ORDER BY

• Go deep into Window Functions: ROW_NUMBER, RANK, LAG, LEAD

• Practice CTEs, Subqueries, and Indexing Basics

• Learn performance tuning (EXPLAIN plans, indexes)

👉 Resources: LeetCode (SQL), Mode Analytics SQL Tutorial, OneCompiler

Python:

• Learn data structures: Lists, Dicts, Tuples

• Write basic ETL scripts using pandas

• Practice reading/writing files (CSV, JSON, API calls)

• Understand exception handling and basic functions

👉 Resources: Dataquest, Jupyter Notebook, Kaggle Datasets

---

My DE Prep Scheduler (SQL + Python)

Here’s how I structured my own study plan when I first applied for Data Engineering roles. No fluff. Just habits that stuck.

---

Week 1 – SQL Core + Python Setup

Goal: Build confidence with basic SQL and get Python environment ready.

Mon–Tue:

• SQL: SELECT, WHERE, GROUP BY, ORDER BY

• Resource: Mode SQL Tutorial + OneCompiler (hands-on)

• Python: Install Python, set up Jupyter Notebook or VS Code

Wed–Thu:

• Python: Data types, loops, functions, lists & dicts

• SQL: Write queries using GROUP BY and filters on public datasets (Kaggle or LeetCode)

Friday:

• Mini Project: Analyze a CSV file using pandas and filter top rows by condition

• Output: Save your script and query result snapshot

---

Week 2 – SQL Intermediate + ETL in Python

Goal: Get hands-on with intermediate SQL and write your first ETL script.

Mon–Tue:

• SQL: CTEs, Subqueries (solve 3 problems/day on LeetCode)

• Python: Read/write CSV + JSON files, understand APIs with requests

Wed–Thu:

• Build: ETL Script (Extract COVID API → Clean → Write to local file or SQLite)

• SQL: Join practice + filtering with aliases and subqueries

Friday:

• GitHub Upload: Push your ETL script with README

• Bonus: Create a 1-pager explaining what your script does

---

Week 3 – Window Functions + Real Data Handling

Goal: Use advanced SQL and clean real-world data.

Mon–Tue:

• SQL: ROW_NUMBER, RANK, LAG, LEAD (focus on order + partition logic)

• Python: Use pandas to clean messy real-world data (missing values, data types)

Wed–Thu:

• Practice: NYC Taxi or Netflix dataset — clean with pandas, summarize with SQL

• SQL: Practice use cases like top 3 per category, running totals

Friday:

• Output: Save your cleaned dataset, sample queries, and explain what insights you found

---

Week 4 – Optimization + Portfolio Building

Goal: Wrap Month 1 with real proof of work.

Mon–Tue:

• SQL: Learn EXPLAIN plan, indexing, query performance basics

• Python: Add logging, exception handling to your ETL script

Wed–Thu:

• Portfolio Time: Write a full README for your project

• Push code + screenshots to GitHub

Friday:

• Reflection:

  • What did you learn?

  • What would you do differently?

  • What’s the next dataset you want to try?

---

Tools I Used (And Still Recommend):

• SQL Practice: LeetCode SQL, Mode, OneCompiler

• Python IDE: Jupyter Notebook, VS Code

• Data: Kaggle, NYC OpenData, COVID API

• Version Control: GitHub

• ETL Stack: pandas + SQLite (perfect beginner combo)

---

Month 2 – Learn ETL + Orchestration (Real Project Work Begins)

This month is about translating your learning into real pipelines. You’ll now build actual systems that move and transform data.

Choose 1–2 projects below based on your tool comfort (local/cloud/dbt/Airflow).
The goal is not to learn everything — it’s to complete one pipeline end-to-end and publish it on GitHub.

---

Project 1: Local CSV to SQLite ETL (Beginner-Friendly, No Cloud)

Stack: Python, pandas, SQLite, cron job (or Airflow optional)

Problem Statement:
Build a pipeline that pulls NYC Taxi data (CSV), cleans it using pandas, and stores it in a local SQLite DB.

Steps:

1. Download public CSV dataset

2. Clean & transform with pandas (fix datatypes, nulls, etc.)

3. Load into a local SQLite DB

4. Schedule pipeline using cron or basic Airflow DAG

Outcome:

• Simple file-based ETL

• Lightweight & fully local

• Teaches end-to-end scripting

---

Project 2: API to Cloud Warehouse with Airflow

Stack: Python, Airflow, AWS S3, BigQuery or Snowflake

Problem Statement:
Pull daily COVID-19 data from a public API, store raw files in S3, process data, and load it into a warehouse.

Steps:

1. Extract from API (requests + JSON)

2. Save raw JSON/CSV to S3

3. Clean data in pandas or dbt

4. Load to Snowflake or BigQuery

5. Orchestrate everything with Airflow DAG

Outcome:

• Full cloud-native ETL pipeline

• Shows data ingestion + orchestration

• Great proof of cloud skillset

---

Project 3: Local CSV to dbt + DuckDB (No Cloud, SQL Focus)

Stack: dbt, DuckDB, CSV, Jinja, SQL

Problem Statement:
Use dbt to build a transformation pipeline that models ecommerce order data from a local CSV.

Steps:

1. Load CSV into DuckDB (acts like a local warehouse)

2. Create staging and mart models with dbt

3. Apply SQL transformations using dbt

4. Generate documentation & DAG visualizations

Outcome:

• Teaches modeling & transformations

• Easy to run locally

• Helps learn dbt structure and SQL best practices

---

Project 4: Reddit Data Pipeline with Python + MongoDB

Stack: Python, PRAW API, MongoDB, Airflow (optional)

Problem Statement:
Extract posts from a subreddit using Reddit’s API and store them into a NoSQL database.

Steps:

1. Authenticate with Reddit using PRAW

2. Extract posts & comments

3. Clean and process text

4. Store into MongoDB

5. Optional: schedule using Airflow

Outcome:

• Exposure to unstructured data

• Real-world use of APIs + NoSQL

• Fun and engaging project for resumes

---

Project 5: Batch to Analytics Dashboard (SQL + Streamlit)

Stack: pandas, SQLite, Streamlit, Matplotlib, SQL

Problem Statement:
Ingest historical sales data and build an analytics dashboard to track trends.

Steps:

1. Ingest CSV files weekly into SQLite

2. Use pandas/SQL to analyze key metrics (revenue, retention, cohort)

3. Build a live dashboard with Streamlit

4. Optional: Automate ingestion with cron job

Outcome:

• Combines data engineering with dashboarding

• Useful for end-user reporting

• Makes you stand out for DE/BI hybrid roles

---

Tip for All Projects:

Each project should include:

• README.md (with overview, stack, steps, and diagram)

• Pipeline code/scripts/notebooks

• Screenshot of output or working dashboard

• Optional: Loom video explaining your pipeline

---

How to Talk About Projects in Interviews

Most candidates freeze here. Don’t.

Here’s how to prep for interview questions about your projects:

• Why did you choose this dataset?

• What challenges did you face while cleaning or transforming it?

• What trade-offs did you make in designing the pipeline?

• How would you scale this pipeline for daily use?

• What would you improve if given more time?

Have clear answers. Show thought process. That’s how you stand out.

---

Common Traps to Avoid

Avoid these if you want to land a job faster:

• Spending 3 months making your README “aesthetic”

• Overbuilding with 6 AWS tools before writing your first ETL script

• Learning SQL, Python, and Airflow in isolation — instead of connecting them in one project

• Binge-watching tutorials without building a single pipeline

• Applying to 100 jobs with no GitHub proof of work

---

One Last Thought:

If you're stuck, it’s not because you're not smart enough. It's because no one ever gave you the real playbook.

This newsletter is that playbook.
If it helped you, forward it to a friend who's trying to get into tech. You never know whose life it might change.

–
Avantikka Penumarty
Ex-META | Snr. Data Engineer | Founder, Zero to Data Engineer
zero2dataengineer.substack.com