Influencing the reach of businesses, deciding recommendations based on your interest, providing offers exactly when you need them, and much more are all accomplished through the proper usage of data. Individuals who wish to make a change in current recommendations or other processes associated with the data have enormous opportunities to make career choices in the field. 

A compiled Data Engineering job description includes data processing, usage enhancement, and appropriateness for the organization. Are you skeptical about what the job would look like? Are you worried about your expectations? We get this and, hence, have you covered. Read on to enlighten yourself.

What Is Data Engineering?

Data engineering refers to developing data collection, processing, and analysis systems while handling their storage and transformation. Adding to the complexity, the data is collected from distinct data sources and formats such as structured, semi-structured, and unstructured. The obtained data is used for future applications to gain insights into business decisions, functions, and goals and objectives. 

Further, the discipline of data engineering also encompasses data quality and data access assurance. They must ensure data quality meets the requirements of professionals who will be receiving the processed data for further functions and analysis.  

What Does a Data Engineer Do?

Data engineers are tasked to perform the functions in distinct settings. Their prime tasks include collecting, managing, and converting raw data to usable forms such as machine-readable formats. It will be converted into a suitable usage form for data scientists and business analysts. They enhance the data quality and reliability to ensure the intended results are delivered. Data engineers are also concerned with data extraction and transformation for prescriptive and predictive modeling. 

Data Engineering Responsibilities

The data engineering responsibilities by the data engineers include: 

  • Perform enhancement in back-end system through agile software development processes 
  • Develop data pipelines for data cleaning, transformation, and aggregation 
  • Develop models for predictive analysis and offer solutions to business problems 
  • Build complex algorithms for data presentation 
  • Analyze raw data and develop and maintain datasets specific to business requirements 
  • Enhance data quality and efficiency 
  • Generate algorithms for transforming data into usable form 
  • Curate business intelligence report 
  • Provide the businesses with novel data validation methods along with appropriate data analysis tools 
  • Optimize data delivery and re-design infrastructure for better stability
  • Collaborate with external and internal stakeholders for assistance in data-based issues

Data Engineering Job Description

The data engineering job requirements encompass the following: 

Data Security And Compliance

Data engineers are responsible for data integrity, availability, and confidentiality. They must prevent data corruption or modification and ensure unauthorized access to confidential data. Access control, data masking, encryption, and audit trails are among the techniques used for this process. 

Data Analysis

Tools like AWS Athena and Power BI are generally demanded proficiently by candidates aspiring to be data engineers. Common data analysis requirements include effective visualization and the ability to interpret and offer conclusions backed by logic. 

Data Pipeline Management

Data pipeline refers to a sequential set of procedures associated with data handling, beginning with data collection and ending with data delivery. The Python-based workflow management system Airflow and another cloud-based data processing service, Cloud Dataflow, are preferred options for data pipeline management. 

Data Warehousing

Data engineers are concerned with optimizing data warehouses, which are associated with data storage and management of data obtained from different sources. The ultimate aim is fast query and data retrieval. Different tools essential for practicing the tasks are Apache Kafka, Teradata, and others. 

Machine Learning

Machine learning is essential for data engineers to manage data processes completely. It includes data preprocessing, model training, and deployment. The popular frameworks commonly used to leverage Machine Learning for Data Engineers are PyTorch, TensorFlow, and Sci-kit Learn. 

Architecture Design

Data engineers are essential for building and managing data systems. They are supposed to ensure the provision of reliable, secure, cost-effective systems. Relevant tools here are CodeSee, ArchiMate, and others. 

Develop Data Set Processes

Developing dataset processes is a full-fledged approach that includes multiple technical steps. It includes data collection, ingestion, storage, management, transformation, preprocessing, versioning, and lineage, followed by monitoring and quality assurance. 

Improve Data Reliability And Quality

Improved data reliability and quality involve regular auditing, adhering to data governance and policies, and validating and cleansing the data. Automated data testing and monitoring are also part of the work to ensure the role's complete functioning. 

Knowledge of Algorithms and Data Structures

Data engineers are required to gain knowledge of algorithms such as sorting, searching, graphing, and Machine Learning algorithms. Further, the list of data structures to work with includes arrays and lists, tables, graphs, and trees. 

Provide Data Access Tools

Data access includes managing users' and applications' access to and interaction with data. It includes several aspects: authentication, authorization, anonymization, data masking, cataloging, and virtualization. Amazon Web Service IAM, Okta, and Apache Atlas are a few of the multiple data access tools used. 

Acquire Data

Data engineers acquire data by connecting with relational and NoSQL databases and data warehouses. The professionals are also responsible for API integrations, streaming data ingestion, web scraping, and data extraction. The tools used include AWS Data Exchange, Selenium, and others. 

Clean data

Data cleaning is an important step in data handling and usage. It includes removing duplicate or irrelevant observations, fixing structural errors, filtering unwanted outliers, handling missing data, validation, and question-and-answer sessions. 

Data APIs

The API or Application Programming Interface refers to rules for communication among different software systems. Data engineers follow the processes mentioned above for APIs, which include data collection, storage, and processing. They also build and deploy APIs, which include implementation and testing.  

Data Integration

Data engineers can process data integration using either of the five approaches. These include ETL, ELT, application integration (API), streaming, and data visualization. Implementation can be done via manual coding through SQL or by setting up and managing data integration tools. 

Data Quality Assurance

This part of the Data Engineer's job involves removing and correcting record formats and duplicates, handling missing data, and standardizing data values. The ultimate aim is to remain consistent and accurate and make the data usable for decision-making purposes. 

Distributed Systems

Some of the techniques used here to handle distributed systems are data partitioning, replication, sharding, caching, load balancing, consistency models, distributed coordination, data lineage, and provenance. 

ETL Processes

ETL, or Extract, Transform, and Load, refers to the process of combining data from different sources. It is done through a data warehouse.  

Data Engineering Skills

The data engineers are required to possess both hard and soft skills. The specific among them are mentioned below: 

Hard Skills 

  • Programming language: Knowledge and experience with multiple programming languages are required to progress throughout the career. However, beginning with one or a few is fine. Python, Ruby, NoSQL, Java, R, C, and C++ are the required programming languages. 
  • Data handling: It includes data warehousing and modeling. The data engineers must be familiar with query optimization, database design, data integrity, schema modeling, and the second and third tiers of the Data Science Hierarchy of Needs. 
  • Data pipeline orchestration: It requires familiarity with data schedule and orchestration tools. 
  • Containerization: Technologies like Docker and container orchestration tools for deploying and managing data engineering applications. 
  • Version control: It is required to manage code changes and team collaboration. 

Soft Skills 

  • Adaptability: It is the key skill required to switch organizations in view of constant and rapid advancements and progress. They must be able to accept new tools, technologies, and methodologies. 
  • Problem-solving: It is required for almost every technical and non-redundant job role. Coming across new challenges in data handling, processing and analysis, it is critical to troubleshoot technical errors while carrying out optimization. 
  • Communication: Data engineers connect with different teams across the organization. Effective communication skills, combined with technical knowledge, are essential for clearly stating the requirements and understanding the provided solutions. 
  • Documentation: Data engineering requires document processing, creating data pipeline diagrams, and providing code notation for knowledge transfer, data system maintenance, and troubleshooting. 

Salary of a Data Engineer

The data engineers get an average salary of around INR 11.8 lakhs per year. The range has lower and upper limits as 7 lakhs and 15 lakhs. There is additional cash compensation of about INR 1.8 lakhs per year. The minimum cash compensation is expected to be around INR 1.72 lakhs, and the maximum cash compensation is expected to be around INR 1.87 lakhs. 

Companies Hiring Data Engineers

Numerous companies are hiring data engineers along with the tech giants. Here are the top 5 multinational companies among them: 

Amazon 

Amazon is a well-known brand name for daily use. Being technologically advanced, the company has a strong presence and hires for different positions. Here are certain related openings the company posted:

  • Senior data engineer, GFP Analytics 
  • Data Engineer, DSP Analytics 
  • Operations Engineering Interns 

Netflix

Having binge-watched on the app, you can also contribute to recommendations of the app. The hirings for the role at Netflix have been posted for the following designations: 

  • Data engineer - Privacy 
  • Data engineer - CKG
  • Data engineer - Ads 
  • Data visualization engineer 

Google 

Striving daily to remain in the news and updated, they hire only quality individuals and professionals. With a low acceptance rate, the interview process in the company is long. The related roles at Google are: 

  • Software Engineer III, Google Cloud Data Management 
  • Data center technician, engineering field services 
  • Data engineer, gTech Analytics, Platforms and Tools 

Flipkart

In the competition with Amazon in retail and marketing, they have established their place in the business. Being a well-known and reputed company, the data engineer requirements posted by the company are as follows: 

  •  Data Engineer I/II
  • Software development engineer 

Microsoft 

Being a computer brand, they also hire Data engineers. The hirings here generally require candidates to work on Office 365, Azure, and others. They have also posted requirements for the following positions in the field: 

  • Senior data engineer 
  • Data Engineer II (semantic models)
  • Senior software engineer - Data engineering 
  • Data Engineer (Fabric)
  • Data engineer - Reliability (Fabric)

With a career in Data Engineering, candidates can either progress in the core field or head towards a managerial role in the same field. The related yet different career paths to take include: 

  • Data Architect: Also referred to as a data systems architect, these professionals are responsible for the organization's data management framework. They work to develop or enhance business architecture while aligning the framework with business architecture. 
  • Data Manager: Handling the managerial role, they supervise the company’s data systems. Their prime function is to ensure efficient organization, storage, and security. They are also responsible for meeting software and hardware needs and ensuring compliance with regulatory standards. 
  • Data Scientist: This is a common, respected, and in-demand profession that requires candidates to leverage data to gain insights and make decisions. They are responsible for finding patterns and trends in datasets, creating algorithms and data models, improving data quality, and communicating the recommendations to the teams. 
  • Database Administrators: They also serve in management positions. Their roles concern database maintenance, security, and operations. They deal with accurate storage and data retrieval while solving any issues. 
  • Data Specialist: The professionals in this job role analyze, sort, collect and develop e-data and information systems. They are concerned with converting data from hard copy to digital format. 

Conclusion 

The article serves as a testament to the increased demands of data engineers in the current market. Indicating their job description and responsibilities, we have also covered the average salary package a Data Engineer can expect. Thus, it is the right time to take action for individuals seeking career opportunities with vast prospects in data engineering. Begin with understanding the basics and building the right set of skills to start your journey with a suitable course program like the Post Graduate Program In Data Engineering.

FAQs

1. Does data engineering require coding?

Yes, coding is essential for data engineering. 

2. Can someone without a technical background become a data engineer?

Yes, anyone can become a data engineer after gaining essential knowledge to take up data engineering responsibilities. 

3. How important is problem-solving in a data engineering role?

With newer problems rising in the engineering data structure, format, handling, and other aspects, problem-solving skills are essential for carrying out routine tasks. 

4. Can data engineers work remotely, or do they need to be on-site?

Yes, they can work from home. 

5. What are the common challenges faced by data engineers?

The challenges faced by data engineers include network or hardware failures, errors in processing tasks, planning scalability, and fault tolerance.

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