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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

9 Must-Have Skills to Become a Data Engineer!

Lakshay arora
Lakshay arora Last Updated : 16 Jul, 2024
9 min read

Introduction

Data Engineering, characterized by its rapid expansion, offers a diverse array of career avenues. With giants like Google, Facebook, Quora, Twitter, and Zomato generating vast volumes of data at an unparalleled rate, the demand for skilled professionals is soaring. Organizations are swiftly adopting Big Data Technology to harness and leverage this influx of data. Are you eager to ride this wave of opportunity? To thrive in this dynamic field, it’s essential to cultivate key data engineer skills, mastering the essential techniques necessary for success. In this article, you will learn about the data engineer skills required for you, also what skills required for the data engineer job description to get.

Table of contents

What is a Data Engineer?

Data engineers play a crucial role in data science projects, focusing on the development and maintenance of data architecture. They ensure seamless data flow between servers and applications, bridging the worlds of software engineering and data science. Their key responsibilities encompass creating data collection methods, integrating new technologies, and optimizing foundational data processes.

Data Engineers are responsible for storing, pre-processing, and making this data usable for other members of the organization. They create the data pipelines that collect the data from multiple resources, transform it, and store it in a more usable form.

Mastering key data engineer skills and essential data engineering skills is crucial for success in the rapidly evolving field of data engineering. These skills empower professionals to navigate complex data landscapes and drive innovation in today’s data-driven world.

Career Growth in Data Engineering

Entering 2023, data engineering emerges as a pivotal and highly rewarding career choice. Recent projections indicate that the global data engineering and big data services sector is set to exceed $77.37 billion by year-end, with a remarkable 17.60% compound annual growth rate.

Furthermore, a LinkedIn survey underscores the industry’s momentum, revealing a 40% surge in job openings for data engineering roles, significantly outpacing the 10% increase in data science positions. Remuneration is equally compelling, with Glassdoor data from May 2022 indicating that the average data engineer earns an annual salary ranging from $115,176 to $168,000. These figures paint a bright future for the field.

Top 9 Data Engineer Skills

Let’s now explore top 9 data engineer skills that you must have to succeed in this field!

Programming Languages

Programming provides us a way to communicate with machines. Do you need to become the best in programming? Not at all. But you will definitely need to be comfortable with it. You will be required to code the ETL process and build data pipelines.

Data Science Skills - Programming

The following programming languages are the most popular among data engineers:

Python

It is one of the easiest to learn a programming language and has the richest library. I have found Python to be a lot easier to perform machine learning tasks, web scraping, pre-process big data using spark, and also is the default language of airflow.

If you want to learn Python, here is a great free course you can refer to our course on Introduction to Python !

Scala

When it comes to data engineering, the spark is one of the most widely used tools and it is written as Scala. Scala is an extension of the Java language. If you are working on a Spark project and want to get the maximum out of the spark framework, Scala is the language you should learn. Some of the spark APIs like GraphX is only available in the Scala language.

Here are some of the recommended resources to get started with:

SQL Databases

You can’t get away from learning about databases when you are aspiring to become a data engineer. In fact, we need to become quite familiar with how to handle databases, how to quickly execute queries, etc. as professionals. There’s just no way around it!

Data Engineer Skills - SQL Databases

SQL databases are relational databases that store data in multiple related tables SQL is a must-have skill for every data professional. Whether you are a data engineer, a Business Intelligence Professional, or a data scientist – you will need Structured Query Language (SQL) in your day to day work.

You should know

  • How to insert, update, and delete records from your database?
  • How to create reports and perform basic analysis using SQL’s aggregate functions?
  • How to perform efficient joins to fetch your data from multiple tables?

Want to get answers to all these questions? Here are some of the best resources to clear your doubts:

NoSQL Databases

The sheer fact that more than 8,500 Tweets and 900 photos on Instagram are uploaded in just one second blows my mind. we are generating data at an unprecedented pace and scale right now in the form of text, images, logs, videos, etc.

To handle this much amount of data, we need a more advanced database system that can run multiple nodes and can store as well as query a huge amount of data. Now, there are multiple types of NoSQL databases, some of them are highly available and some of them are highly consistent. Some are column-based, some are document-based and some are graph-based.

Data engineer skills - NoSQL Databases

As a data engineer, you should know, how to select the appropriate database for your use-case and how to write optimized queries for these databases. Here are some of the resources that will help you get started with the NoSQL databases.

Apache Airflow

Automation of work plays a key role in any industry and it is one of the quickest ways to reach functional efficiency. Apache Airflow is a must-have tool to automate some tasks so that we do not end in the loop of manually doing the same things again and again.

Apache Airflow

Mostly data engineers have to deal with different workflows like collecting data from multiple databases, pre-process it, and upload it. Consequently, it would be great if our daily tasks just automatically trigger on defined time, and all the processes get executed in order. Apache Airflow is one such tool that can be very helpful for you. Whether you are Data Scientist, Data Engineer, or Software Engineer you will definitely find this tool useful.

You can deep dive into some of these concepts with these clear articles and their examples –

Apache Spark

It is the most effective data processing framework in enterprises today. It’s true that the cost of Spark is high as it requires a lot of RAM for in-memory computation but is still a hot favorite among Data Scientists and Big Data Engineers.

Apache Spark

Organizations that typically relied on Map Reduce-like frameworks are now shifting to the Apache Spark framework. Spark not only performs in-memory computing but it’s 100 times faster than Map Reduce frameworks like Hadoop.

It provides support in multiple languages like R, Python, Java & Scala. It also provides a framework to process structures data, streaming data, graph data. You can also train machine learning models on big data and create ML pipelines.

If you want to learn spark, here are some resources you can refer to –

ELK Stack

It is an amazing collection of three open-source products — Elasticsearch, Logstash, and Kibana.

ELK Stack
  • ElasticSearch: It is another kind of NoSQL database. It allows you to store, search, and analyze a big volume of data. If the full-text search is a part of your use case, ElasticSearch will be the best fit for your tech stack. It even allows search with fuzzy matching.
  • Logstash: It is a data collection pipeline tool. It can collect data from almost every resource and makes it available for further use.
  • Kibana: It is a data visualization tool that can be used to visualize the elasticsearch documents in a variety of charts, tables, and maps.

More than 3000 companies are using ELK stack in their tech stack, including Slack, Udemy, Medium, and Stackoverflow. Here are some of the free resources from where you can start learning ELK Stack.

Hadoop Ecosystem

Hadoop is a complete eco-system of open source projects that provide us the framework to deal with big data.

We know that we are generating data at a ferocious pace and in all kinds of formats is what we call today as Big data. But it is not feasible storing this data on the traditional systems that we have been using for over 40 years. To handle this massive data we need a much more complex framework consisting of not just one, but multiple components handling different operations.

Hadoop Ecosystem

We refer to this framework as Hadoop and together with all its components, we call it the Hadoop Ecosystem. Here are some of the free resources to know more about Hadoop.

Apache Kafka

Tracking, analyzing, and processing real-time data has become a necessity for many businesses these days. Needless to say, handling streaming data sets is becoming one of the most crucial and sought skills for Data Engineers and Scientists.

Apache Kafka

We know that some insights are more valuable just after an event happened and they tend to lose their value with time. Think of any sporting event for example – we want to see instant analysis, instant statistical insights, to truly enjoy the game at that moment, right?

For example, let’s say you’re watching a thrilling tennis match between Roger Federer v Novak Djokovic.

The game is tied at 2 sets all and you want to understand the percentages of serves Federer has returned on his backhand as compared to his career average. Would it make sense to see that a few days later or at that moment before the deciding set begins?

Kafka is a much-needed skill in the industry and will help you land your next Key Data Engineer Skills role if you can master it. Here are some of the resources that you can refer to:

Amazon Redshift

AWS is Amazon’s cloud computing platform. It has the largest market share of any cloud platform. Redshift is a data warehouse system, a relational database designed for query and analysis. You can query petabytes of structured and semi-structured data easily with Redshift.

Amazon Redshift

Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. Most of the data engineering job descriptions specifically list Redshift as a requirement.

Here are some of the resources to get started with the Amazon Redshift.

Conclusion

In conclusion, the world of data engineering thrives on essential skills like database management, ETL processes, and programming languages. As industries evolve in the data-driven landscape, mastering these key data engineer skills becomes paramount for success. Whether entering the field or enhancing expertise, investing in these abilities unlocks doors to endless opportunities in the data-driven era.

Hope you like the article and getting understanding about the data engineer skills and what skills required for the data engineer job description.

Do you have any other skills that you wish were on this list to become a data engineer? Let me know in the comments!

Frequently Asked Questions

Q1. What are Key Data Engineer Skills?

A. Data engineers need skills in database management, ETL processes, data modeling, data warehousing, and programming languages, along with a grasp of relevant tools and technologies.

Q2. What are Essential Data Engineering Skill?

A. Data engineers should possess hard skills in SQL, NoSQL, Hadoop, Spark, data pipeline development, data integration, scripting languages (e.g., Python), and familiarity with cloud platforms (e.g., AWS, Azure, GCP).

Q3. Do data engineers need coding skills?

A. Yes, data engineers require coding skills. Proficiency in languages like Python, Java, or Scala is essential for tasks like building data pipelines and automating processes in data engineering.

Q4. What is the typical role of a data engineer?

A. The typical role of a data engineer involves designing and maintaining data architectures, creating ETL pipelines, ensuring data quality, collaborating with data scientists and analysts, and optimizing data storage for efficient analytics and reporting.

Lakshay arora
Lakshay arora

Ideas have always excited me. The fact that we could dream of something and bring it to reality fascinates me. Computer Science provides me a window to do exactly that. I love programming and use it to solve problems and a beginner in the field of Data Science.

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