Become a Data Engineer
Everything you need to know; What data engineers do, what tools they use, and exactly how to start building your skills.
Code. Report. Analyse. Excel
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What Does a Data Engineers Actually Do?
Data Engineers are the architects of the data world. They build and maintain the pipelines that collect, transform, and deliver clean, reliable, and fast data across systems.
Architect & Optimise Data Systems (20 - 25%)
You’ll help design scalable, efficient, and secure data infrastructure. This could involve data modeling, partitioning datasets for performance, managing permissions, and creating reusable data products. Organising data pipelines in a simple is key for optimisation.
Working Example: Designing a new event schema in Snowflake, ensuring tables are indexed for fast querying, and automated refreshes happen nightly.
Monitor, Debug & Improve Data Quality (15 - 20%)
Pipelines break. Data gets messy. A big part of your time is spent testing, validating, and fixing issues. You’ll write data tests, monitor performance, and create alerts for anomalies. Periodically re-engineering pipelines will ensure ongoing performance.
Working Example: After a marketing tool update, your pipeline starts failing; you trace the schema change, update your parser, and reprocess missed records.
Collaborate & Define Requirements (20 - 25%)
You’ll work closely with data analysts, scientists, and business leads to understand what data is needed, how it should be structured, and what problems the data needs to solve. Engineers often define what’s even possible, or how to build toward it.
Working Example: A product team wants daily churn analysis. You define how to capture that data from a CRM system, and where to store it.
Design, Build & Maintain Data Pipelines (35 - 40%)
This is core to the job; creating and maintaining data pipelines that ingest, transform, and load data into storage areas like data warehouses. You'll use SQL, Python, and orchestration tools to ensure data is accurately and reliably tranferred to final data tables.
Working Example: Building an ETL pipeline that extracts transactions from an API, transforms them in Python, and loads them into Databricks for analysis.
Who Do Data Engineers Work With?
You’re not just coding in isolation. Data Engineers are deeply embedded in the data workflow, and collaboration across departments to ensure data is essential. You’ll work with:
Data Analysts & Scientists - they rely on you for accurate, well-structured data
Product Managers & Founders - they need your expertise to understand what's possible
BI & Ops Teams - You’ll automate pipelines, support dashboards, and troubleshoot issues
IT & Technical Teams - to ensure security and infrastructure is prepared for a data landscape
Foundational Skills
SQL
Every data pipeline starts with data extraction, and SQL is one of the primary ways to do it. Even if you’re later working in Python or with tools like Spark, writing efficient SQL is a must for staging data before transformation.
Why It Matters?
These are the core skills you’ll need to become job ready, and we've provided some recommended resources to help get you prepared
Data Orchestration
Orchestration tools like Azure Data Factory (ADF), Airflow, and Fabric allow you to automate and schedule ETL/ELT workflows. These tools manage complex sequences of tasks across systems, without manual intervention.
Where to Start
Why It Matters?
SQL Performance
Where to Start
Azure Data Engineering
Extracting tables from a transactional database
Writing CTEs for time-based aggregations
Validatin row counts across systems
Designing lean, efficient data warehouse structure
Automating transformation with procedures
Real World Use Cases
Pro Tip
Focus on mastering window functions, partitioning, and query optimisation; these show up constantly in engineering interviews and production jobs.
SQL Bolt
Advanced SQL
Real World Use Cases
Pro Tip
Automating daily refresh from APIs into SQL
Triggering data validation after a load completes
Coordinating multiple pipelines across systems
Creating reportable views from large datasets
Creating alerts for activity monitoring
Use parameterised pipelines and dynamic datasets; they make ADF/Airflow jobs reusable, scalable, and efficient.
Engineering on AWS
ADF for Engineers
Python
Python is the most flexible language for automating data workflows. It’s used to clean, transform, and move data between sources. Workflows that can transition into different languages to serve separate orchestration functions are powerful.
Why It Matters?
Data Warehousing
Data engineers build the structures where analysts and applications can access data reliably. You need to understand data models, warehouse architecture, and cloud-native platforms, to ensure scalability and future proofing.
Why It Matters?
Writing ETL scripts that pull data via API
Using Pandas to preprocess CSVs or JSON logs
Scheduling batch jobs with Python + Airflow
Creating helpers to join data in databricks
Logging results in tables for later audit
Real World Use Cases
Pro Tip
Learn how to write reusable functions and logging into your scripts early; it pays off as your projects scale.
Real World Use Cases
Pro Tip
Designing star schemas in Snowflake or SSMS
Noticing when averages hide outliers
Digging into causes of results change
Spotting opportunities in weekly sales data
Talking confidently to all stakeholders
Understanding dimensional modeling (facts vs dimensions) helps you build for query speed and usability, not just storage.
Where to Start
Python for Engineering
Where to Start
Data Warehouse Toolkit
Python ETL Projects
Google Colab
Snowflake 101
Google Cloud Engineering
Advanced Skills
Advanced SQL
Basic SQL gets you through simple extracts. Advanced SQL for things like window functions, CTEs, optimisation techniques, and indexing is how you handle billions of rows efficiently. These skills separate junior engineers from experienced ones
Why It Matters?
These are the aspirationl skills you’ll need to excel as a Data Engineer or prepare to transition into a more advanced role
Advanced Reporting
As companies grow, analysts can’t handle every request. Engineers build data marts, semantic layers, and reusable views that allow non-technical users to generate insights without waiting. Creating clean, easy to interpret datasets helps with all level of collaborator
Where to Start
Why It Matters?
SQL Performance
Where to Start
Python for Data Analysts
Writing Multi level aggregations for reporting
Debugging and tuning slow running queries
Using PARTITION BY and RANK for churn
Dynamically reconciling data through automation
Logging execution runs for later audit
Real World Use Cases
Pro Tip
Use EXPLAIN or QUERY PLAN tools, they reveal where your queries bottleneck before deployment, helping to optimise first.
Advanced SQL
Real World Use Cases
Pro Tip
Creating pre-aggregated views for Power BI
Building dbt models that analysts can extend
Automating KPI refresh logic for exec dashboards
Allowing dashboard input to backfill datasets
Embed in various apps for curated views
The best pipelines serve business users; think like a product manager, not just a coder to ensure data is used and effective.
Reporting Bootcamp
Cloud Data Tools
Modern data engineers are cloud-native. Whether you're batch processing with Spark, building notebooks in Databricks, or managing serverless ETL jobs. These are the operational tools of enterprise-scale infrastructure and analytics.
Why It Matters?
Version Control
Data teams now operate like software teams. Using Git to manage your pipeline code, version transformations, and collaborate via pull requests brings clear alignment and responsibilities across data teams, and allows visibility to manage more easily.
Why It Matters?
Using Spark to transform large datasets
Building ML-ready data pipelines in Databricks
Using AWS Lambda to trigger ETL after an S3 drop
Using notebooks for multi language tranformation
Reconciliation pipelines to ensure accuracy
Real World Use Cases
Pro Tip
Pair something like Databricks with Delta Lake to make your big data pipelines reliable, ACID-compliant, and lightning fast.
Real World Use Cases
Pro Tip
Managing dbt model changes across dev and prod
Using branches to test experimental logic safely
Reviewing peer pipeline edits before merge
Aligning Approval process for confident merges
Rolling back when careless approvals happen
Adopt Git workflows early, even if you're working solo. It’s good practice and acts as a safety net when pipelines fail.
Where to Start
Databricks Engineering
Where to Start
Git Pocket Guide
Databricks essentials
Git & GitHub Bootcamp
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