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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

Data Engineer Job Description, Responsibilities & Role

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Analytics Vidhya Last Updated : 16 Jul, 2023
8 min read

In a data-driven world, behind-the-scenes heroes like data engineers play a crucial role in ensuring smooth data flow. Imagine being an online shopper who suddenly receives irrelevant recommendations. A data engineer investigates the issue, identifies a glitch in the e-commerce platform’s data funnel, and swiftly implements seamless data pipelines. While data scientists and analysts receive attention, it is the relentless work of data engineers that ensures accessible and well-prepared data for informed decision-making within organizations. What exactly does a data engineer do? How do they contribute to business success? Let’s dive into their world, explore the data engineer job description, roles, responsibilities, and answer your burning questions.

Table of contents

What is a Data Engineer?

Data engineers play a critical role in gathering, managing, and transforming raw data into valuable insights for business analysts and data scientists. Their main objective is to ensure data accessibility, enabling companies to optimize performance and make informed decisions. They design algorithms, analyze statistics, and align data systems with business objectives to maximize efficiency. Data engineers need strong analytical skills, the ability to integrate data from diverse sources, proficiency in programming languages, and knowledge of machine learning techniques. The data engineer job description is expansive, encompassing a wide range of roles and responsibilities that contribute to the data-driven success of organizations.

Sample Data Engineer JD

Data Engineer JD
Source: BeamJobs

Data Engineer Roles and Responsibilities

The data engineer roles and responsibilities may differ from one company to another based on their requirements. However, some common data engineer responsibilities include the following: 

  • Develop and maintain a perfect data pipeline design. 
  • Improve internal processes by identifying, planning, and putting them into practice. Examples include automating manual operations, improving data delivery, re-designing infrastructure for increased scalability, etc.
  • Create the infrastructure essential for effective data extraction, conversion, and loading from a wide range of data sources utilizing SQL and AWS big data technologies.
  • Create massive, complicated data sets that satisfy both functional and non-functional business objectives.
  • Construct analytics solutions that utilize the data funnel to offer actionable insights into gaining new customers, improving operational effectiveness, and other crucial company performance indicators.
  • Assist stakeholders, such as the Executive, Product, Data, and Design teams, with technical data-related challenges and provide for their data infrastructure requirements.
  • Maintain the privacy and security of data across international borders by using several data centers and AWS regions.
  • Develop data tools for the analytics and data scientist team members to help them develop and optimize our product into a cutting-edge market leader.
  • Work with data and analytics professionals to improve the operation of our data systems.

Also Read: Job Comparison – Data Scientist vs Data Engineer vs Statistician

Skills Required to Become a Data Engineer

Data Engineer Skills
Source: AltexSoft.com

If you aspire to become a data engineer, you must be acquainted with certain technical and soft skills. 

Technical Skills

To excel in their role, data engineers should possess the following technical skills:

Coding

Proficiency in programming languages like Python, Java, SQL, NoSQL, Ruby, Perl, MatLab, R, SAS, C and C++, Scala, and Golang is highly preferred by most companies. A solid foundation in coding is essential for data engineering positions.

Understanding of Operating Systems

Data engineers should have a deep understanding of operating systems such as Microsoft Windows, Solaris, Linux, Apple macOS, and UNIX. Each operating system offers unique benefits and caters to specific requirements. Familiarity with different systems can contribute to success in the field.

Data Warehousing

Data engineers are responsible for analyzing and storing large volumes of data. Therefore, they must be well-versed in data warehousing solutions like Redshift or Panoply. Experience in analyzing and managing data using data warehousing solutions is highly valued.

Data Analysis

In-depth knowledge of analytics software, particularly Apache Hadoop-based tools like MapReduce, Pig, Hive, and HBase, is desirable for data engineers. Proficiency in data analysis techniques and tools enables effective data processing and manipulation.

Database Systems

A strong understanding of database management is crucial for data engineers. SQL (Structured Query Language) is the most widely used database solution, so proficiency in SQL is essential. However, knowledge of other database solutions like Cassandra or Bigtable can benefit freelance data engineers.

Basic Knowledge of Machine Learning

While machine learning is the domain of data scientists, data engineers should have a foundational understanding of utilizing different data types in machine learning applications. Building knowledge in statistical analysis and data modeling can make data engineers valuable assets within organizations and set them apart in their field.

Soft Skills

To excel as a data engineer, it’s important to develop strong, soft skills in addition to technical expertise:

Communication Skills

Effective communication is essential as data engineers collaborate with colleagues from diverse backgrounds, including those without technical expertise. Being able to convey ideas and findings is crucial, especially when working with data architects and data scientists. Strong communication skills are also valuable in remote work environments.

Critical Thinking Skills

Data engineers need to think critically to identify and solve complex problems. Analyzing situations, thinking creatively, and developing innovative solutions is vital. Critical thinking is also applied in troubleshooting and designing data systems to address challenges effectively.

Problem-Solving

Employers value candidates who can tackle issues efficiently and proactively. Data engineers should be able to draw on their industry knowledge to address immediate problems and conduct thorough research or collaborate with colleagues to find long-term, scalable solutions. Strong problem-solving abilities are highly regarded in this role.

Check out – Cheatsheet for Data Engineers

Why Pursue a Career in Data Engineering?

The data engineering profession could be both lucrative and demanding. You will serve a significant part in a company’s success, offering easier access to data that decision-makers, data scientists, and analysts need to perform their jobs. Your problem-solving and programming skills will help you develop scalable solutions. 

Data engineers will stay in demand until there is a need for data processing, which, needless to say, will remain forever. According to Dice Insights 2019 report, data engineering topped the list of trending jobs in the technology sector, followed by computer scientists, web designers, and database architects. 

How Data Engineers Bring Value to Organizations?

Including the database, data engineers extract and obtain data from multiple sources. Such sources can include Oracle Database, SQL Server, Excel, MySQL, or any other data processing or storing software. Thereafter, they employ algorithms on this data to make it functional so that it can help various departments like sales, marketing, finance, and others to increase productivity. 

Data engineers are liable for managing an organization’s analytics. They equip your data with speed. Often, it’s difficult for businesses to make real-time decisions and precisely estimate metrics such as customer retention, churn, and fraud. For example, data engineers can assist an e-commerce firm to discover which of their products will gain higher demand in the future. Likewise, it will help them to target different buyer personas and render more tailored experiences to their consumers. 

Since the world is inclining more toward big data, data engineer certification can drive and use it to create more precise projections. Data engineers can enhance data models and machine learning by offering well-governed data funnels. 

Data Engineer Salary

According to Glassdoor, a data engineer’s average base salary in a year in the United States is around USD 97,848. However, the figure can vary based on various factors, such as the years of experience, the location of the company, and the organization’s size. 

Average annual base and total pay of Data Engineer in the US
Source: Glassdoor

In India, the average base pay of a data engineer is ₹8,75,000 per year. Again, the salary may increase on decrease based on the factors described above. 

For example, in metropolitan cities like Mumbai, Bangalore, and Delhi, a fresher in the data engineering profession can make an average base salary of around ₹7,00,000 – ₹9,00,000 annually. However, candidates with 4 to 6 years of experience can earn between ₹12,00,000 – ₹16,00,000 annually. 

On the contrary, professionals in smaller cities and towns will get a lower average base salary. 

Data Engineer Career Path

Similar to any profession, the positions and roles of data engineers also progress. In fact, various data engineers begin their careers as business intelligence analysts or software engineers. However, over time and with experience, they secure managerial roles or grow into a solutions architect, data architect, or machine learning engineer. Accordingly, data engineer job descriptions also become diverse, than just extracting, processing, and analyzing data. 

How to Become a Data Engineer?

Now that you are acquainted with the data engineer job description, let’s find out how to become a data engineer. Follow the steps below:

Step 1: Polish Your Data Engineering Skills

The first thing you need to do is to develop data engineering skills that include coding, cloud computing, database design, and coding skills. 

In coding, you must know JAVA, Python, SQL, NoSQL, Scala, and R. Moreover, you should be well-versed with relational and non-relational databases and must know how they work. 

Step 2: Acquire Certifications

An excellent way to improve your knowledge and abilities is to study for a certification test. This will allow prospective employers to verify your level of experience. The possibilities for certification include Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, and Google Cloud Certified Professional Data Engineer.

Check for data engineer job descriptions of different companies, you will know what they actually want. 

Step 3: Create a Portfolio of Data Engineering Projects 

Not only in data engineering but in most jobs, including Graphic Designing, Content Development, Website Designing, etc., a portfolio is a must in a job search to showcase your projects and work you have done previously. 

Using a platform like Wix or Squarespace, you may upload data engineering projects you’ve accomplished on your own or as a requirement for coursework to a portfolio website. A free alternative to a standalone portfolio website is to upload your work to the Projects area of your LinkedIn profile or a website like GitHub.

Step 4: Begin with an Entry-Level Role

Many data engineers begin their careers in entry-level positions like database administrator or business intelligence analyst. You can learn new talents and become qualified for more specialized professions as you acquire experience. 

Conclusion 

From the above discussion, one thing is sure- the world, especially of that large-scale businesses couldn’t operate seamlessly without data engineers. They are the heroes working behind the sets of big databases to ensure the proper flow of data through the pipeline. They ensure data is accessible, organized, and prepared to fuel thoughtful decision-making within companies. Moreover, they develop and analyze architectures that enable data extraction and conversion for prescriptive and predictive modeling.

Explore 50+ real word projects with guided sessions with our Blackbelt Plus program.

Frequently Asked Questions

Q1. What does a Data Engineer do?

A. Data Engineers are entitled to convert raw data into information that can be used for decision-making and analytics in business. They operate with several systems that one can find everywhere, including schools, hospitals, or businesses. Therefore, companies have access to the most accurate performance indicators for all of their segments. 

Q2. Who does a data engineer work with?

A. Data engineers collaborate with data scientists to upgrade the accuracy and quality of the information, allowing businesses to make accountable judgments. Data engineers also engage with leaders across the company to assist in business decisions. 

Q3. What are the data engineer roles and responsibilities?

A. Data engineers employ various technologies and tools to accumulate and map a company’s data terrain to aid decision-makers figure optimization and cost savings opportunities. Moreover, they utilize this data for showcasing trends in gathered analytics information, which promotes transparency with stakeholders. 

Q4. Is coding a vital skill for data engineers?

A. Yes, it is obligatory. Data engineers must be acquainted with common coding languages, such as Python, Java, R, SQL, NoSQL, and Scala. 

Q5. Is data engineering a great career?

A. Well, the answer is quite subjective. Data engineering is an in-demand career offering more than average pay and relative job security. As per Glassdoor, the data engineer’s median salary in the United States is $117,545 annually, which is surely lucrative. Even in India, data engineers earn more than INR 14 lakh per annum. 

data engineerdata engineer job descriptionData Engineer Roles
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Top 10 Data Analytics Projects with Source Codes
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Crucial Data Engineer Skills for a Successful Career
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