Data Engineers are at the forefront of technology, turning raw data into powerful insights, shaping a data-driven future across various industries, and offering innumerable opportunities for innovation and growth. Continue Reading
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Explore Career Data Engineers are at the forefront of technology, turning raw data into powerful insights, shaping a data-driven future across various industries, and offering innumerable opportunities for innovation and growth.
What are the typical responsibilities of a Data Engineer?
A Data Engineer would typically need to:
Data Engineers may work in traditional office settings, remotely, or in hybrid work environments depending on their organisational demands. You will spend the majority of your time on a computer/laptop in a collaborative setup, including data analysts, data scientists, business analysts, and professionals from the marketing, finance, or operations departments.
Spending time in data centres and server rooms is commonplace. Travel may be occasionally required to address projects on-site at client locations, attend conferences, and participate in seminars or workshops.
Data Engineers generally work full-time 40-45 hours from Monday to Friday. There can be exceptions with specific roles that require you to work evenings or weekends to meet project deadlines. Remote positions offer more flexibility.
Research suggests that flexible hours and generous telework policies appeal more than salary to the younger generation. There has been an incremental increase in employers willing to give promising employees a chance to adjust their schedules per the work pressure.
Finding a new job may be challenging. Data Engineers can boost their job search by asking their network for referrals, contacting companies directly, using job search platforms, attending job fairs, leveraging social media and inquiring at staffing agencies.
Data Engineers are generally employed by:
Professional associations and organisations, such as the International Data Engineering And Science Association (IDEAS), are crucial for Data Engineers interested in pursuing professional development or connecting with like-minded professionals in their industry or occupation.
Professional associations provide members with continuing education, networking opportunities and mentorship services. Membership in one or more adds value to your resume while bolstering your credentials and qualifications.
Aspiring Data Engineers can begin working towards their goals as early as high school. Gain proficiency in various programming languages, including Java, Scala, Python, and SQL, along with high-performing languages such as C, C#, and Golang. It will help you to start a portfolio, showcasing your practical knowledge of data engineering projects, open-source projects, hackathons, and coding competitions.
Research and identify reputable post-secondary computer science, software engineering, or data science programmes and certifications. The exploration will help you decide your education and training options after high school. Read about the profession and interview/shadow expert Data Engineers to prove your commitment to course providers and prospective employers.
Internships in a university, research institution, government agency, tech company, non-profit organisation, or startup can help you gain real-world experience from professors and researchers while applying what you have learned inside the classroom. Make the most of the internship by actively contributing to research projects or data engineering initiatives, networking with professionals, and learning from the experts.
Your educational provider’s career service office can provide information about relevant on- and off-campus academic internships in diverse sectors. In addition to academic job boards and university-specific job portals, you may get valuable insights into internship opportunities from your academic advisors. Data-related conferences and networking events may host internship fairs.
Some Data Engineers may have access to a Foundation Apprenticeship and get a head start in their chosen profession.
Aspiring Data Engineers acquire a Higher National Diploma (HND) or bachelor’s degree in data science, computer science, computer software engineering, economics, mathematics, applied mathematics, statistics, or information technology (IT).
Whatever your major, make sure your coursework includes software design, computer programming, data architecture, database management, and data structures. Employers may prefer candidates who are proficient in programming languages such as Python, Java, and SQL, big data technologies such as Hadoop, Spark, and Kafka, and cloud computing platforms such as Google Cloud Platform, Azure, and AWS.
Although not mandatory, the experience and expertise you gain from a relevant postgraduate degree or doctorate in data science, quantitative science, computing, economics, or business administration may help your career prospects.
However, even without a relevant degree, you can acquire the necessary programming languages, data tools, machine learning, and statistical skills through an intensive and immersive data science boot camp that may be held online or offline, part-time or full-time, and prove affordable in comparison to a college degree.
Recommended high school courses include mathematics, computer science, physics, IT, and electives related to data analysis, data science, or machine learning, if available. English and speech classes will help you develop your research, writing, and oral communication skills.
Remember that completing a particular academic course does not guarantee entry into the profession. However, your professional qualifications and transferable skills may open up more than one door.
Do your homework and look into all available options for education and employment before enrolling in a specific programme. Reliable sources that help you make an educated decision include associations and employers in your field.
Accredited certification in data engineering, data management and analytics tools, and ETL (extract, transform, and load) tools demonstrates a Data Engineer’s competency in a relevant skill set, typically through work experience, training and passing an examination. Successful certification programs protect public welfare by incorporating a Code of Ethics.
When acquired through an objective and reputed organisation, certification in cloud computing platforms, master data platforms, and data warehousing tools can also help you stand out among candidates with a basic understanding of data science and earn a significant salary premium of up to 18 per cent.
Data Engineers may also need to undergo an employment background check, including but not limited to a person’s work history, education, credit history, motor vehicle reports (MVRs), criminal record, medical history, use of social media and drug screening.
The exact job titles and hierarchy may vary across companies and industries. Larger organisations may have separate roles for the different aspects of data engineering, while smaller companies may have one or two employees handling the entire process. However, the titles along the most common career paths available to qualified Data Engineers include the following.
Fresh graduates typically begin their careers in IT Assistant positions in small companies where they can acquire the requisite experience with programming languages, databases, and big data technologies to move into entry-level Data Engineer roles.
It is also possible that you begin as a Software Engineer/Data Analyst/Business Intelligence Analyst before rising to the position of Data Engineer. Once you prove your expertise and commitment to your employer, you can be promoted to Junior Data Engineer, Senior Data Engineer, and Lead Data Engineer, in that order.
Executive-level roles, such as Data Infrastructure Manager, Head of Data Engineering, or Chief Data Officer, may come your way once you demonstrate the necessary technical expertise and leadership skills. If related fields interest you, consider becoming a Data Architect, Solutions Architect, or Machine Learning Engineer.
The desire to accelerate career growth and personal development has an increasing number of millennials choosing to job hop and build a scattershot resume that showcases ambition, motivation and the desire to learn a broad range of skills. Studies prove that job hopping, earlier dismissed as “flaky” behaviour, can lead to greater job fulfilment.
Employees searching for a positive culture and exciting work are willing to try out various roles and workplaces and learn valuable and transferable skills along the way. However, sustained loyalty to the organisation where you began your career may also work in your favour.
Data Engineers who have entrepreneurial ambitions can open their own data engineering consulting business after gaining adequate experience, business contacts, and resources.
Data Engineers with fluency in data scripting languages and proficiency in data management using industry-standard practices have the best job prospects.
Continuing professional development (CPD) will help an active Data Engineer build personal skills and proficiency through work-based learning, a professional activity,
formal education or self-directed learning.
Most Data Engineers receive on-the-job training to understand the company’s technological tools and data functioning. Keep up with the latest trends and technologies in cybersecurity to stay ahead of the curve. Attend conferences, sign up for webinars, and participate in workshops by industry experts to maintain the competitive lead. Online communities, meetups, and online platforms such as LinkedIn will also allow you to network with like-minded professionals and learn about new opportunities in the field.
In addition to offering the opportunity to continually upskill, regardless of one’s age, job, or level of knowledge, CPD also enables the periodic renewal of desirable certifications, which increase one’s chances of advancement and becoming an independent consultant.
Terms Worth Knowing
A data pipeline is a sequence of data processing stages. It begins with adding data to the platform. The data processed at each stage becomes the input for the next step until the pipeline finishes. Sometimes, separate stages run simultaneously. Data infrastructure includes hardware, software, networking, services, and policies that support data use, storage, and sharing.
A data source can be the point where data originates or is first digitised. However, even highly processed data can be a source if another process uses it. A data source could be a database, a file, real-time data from devices, web scraping, or various online static and streaming data services.
A database schema is like the blueprint for a database. The diagrams represent the logical structure of the entire database, specifying how data is organised and interconnected. It also sets rules and restrictions for the data.
ETL, or "Extract, Transform, Load," integrates data and is popular in constructing data warehouses. The process involves extracting data from source systems, transforming it into a format suitable for analysis, and then loading it into a data warehouse or another system. An alternative approach known as "Extract, Load, Transform (ELT)" focuses on processing within the database to enhance performance.
A database is a structured collection of information or data, typically stored in a computer system and managed by a database management system (DBMS). A data warehouse is a specialised data management system created to facilitate and enhance business intelligence (BI) operations, particularly analytics. Data warehouses conduct queries and analysis and store significant amounts of historical data gathered from diverse sources, including application log files and transaction applications.
A data lake is a central storage place for structured and unstructured data. You can save the data without formatting it beforehand and then perform various types of analytics, including dashboards, visualisations, big data processing, real-time analytics, and machine learning, to inform more intelligent decision-making.
A dataset is a collection of data presented in a tabular format, with each column representing a specific variable and each row corresponding to a particular data point. Datasets are essential in data management and can describe values for variables like height, weight, temperature, or random numbers. The individual values in a dataset are referred to as "data points" or "data."
A machine learning (ML) model is software capable of identifying patterns or making decisions when presented with new, unseen data. In natural language processing, these models can analyse and correctly understand the meaning behind sentences or word combinations they haven't encountered before.
Metadata refers to information about data, enhancing its usability and management. Metadata comes in various forms, depending on its purpose, format, quality, and quantity. Common categories include descriptive, structural, administrative, and statistical metadata.
Understanding the Distinction
Data Scientists are the team’s senior members and require deep expertise in machine learning, statistics, and data handling to turn inputs from Data Analysts and Data Engineers into actionable insights. Data Analysts occupy entry-level positions in data analytics teams and excel in translating numeric data into understandable information for the entire organisation. Data Engineers work with Big Data and compile reports to act as intermediaries between Data Analysts and Data Scientists.
Data Architects and Data Engineers usually collaborate within the same team. However, Data Architects create a data framework vision, while Data Engineers bring this vision to life through a physical framework. Data Architects emphasise data modelling and integration, whereas Data Engineers concentrate on software programming.
Tools Often Used
Data Engineers use integration tools such as Apache NiFi and Apache Kafka to manage data ingestion, transformation, and routing for the smooth flow of data. Data storage solutions, including Amazon S3, Amazon Redshift, and Snowflake, store data reliably and are helpful for analytics. Analytical system tools such as Tableau and Databricks help analyse and visualise data, enabling organisations to gain insights. Data Engineers select these tools based on project needs and their tech environment.
Current Scenario
The employment outlook of a particular profession may be impacted by diverse factors, such as the time of year, location, employment turnover, occupational growth, size of the occupation and industry-specific trends and events that affect overall employment.
There is a predicted rise in demand for Data Engineers over the next three years. Finance, insurance, IT, and professional services sectors are expected to have the most job openings. Interestingly, the need for Data Engineers is surpassing that of data scientists because they focus on data infrastructure security and smooth operation.
Potential Pros & Cons of Freelancing vs Full-Time Employment
Freelancing Data Engineers have more flexible work schedules and locations. They fully own the business and can select their projects and clients. However, they experience inconsistent work and cash flow, which means more responsibility, effort and risk.
On the other hand, full-time Data Engineers have company-sponsored health benefits, insurance and retirement plans. They have job security with a fixed, reliable source of income and guidance from their bosses. Yet, they may experience boredom due to a lack of flexibility, ownership and variety.
When deciding between freelancing or being a full-time employee, consider the pros and cons to see what works best for you.
Despite the multiple challenges they face in mining and analysing Big Data to inform profitable business decisions, becoming a Data Engineer is the best career choice for someone who loves mathematics, programming, and information technology.
Ask the 5 Ws to get a clear and complete set of resources, requirements, and timelines before beginning a project.
From social media to emails and images, around 80% of data needs to be more structured. It requires skilled Data Engineers to decode and structure it in a way that can be used for analytics and decision-making.
What do Data Engineers do?
A Data Engineer would typically need to:
Accredited
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