As businesses eagerly digitize every data point across their ecosystem, proving that data is the new oil, one can't help but ask: To what end?
While organizations of all sizes harness diverse data for analytics solutions that provide prescriptive guidance and aid in strategy building, can this new oil and its engines provide a truly sustainable competitive edge?
The answer lies in Data engineering—how you collect and prepare data so that it can unlock rich, unique insights that help your organization outperform competitors. Formally speaking, data engineering is a crucial function that leverages technologies and best practice frameworks to gather any internal or external stakeholder or ecosystem data relevant to the company's success. This data is transferred and modified into "pipelines" that are completely fit for purpose to underpin analytics solutions of any level of advancement used by the business. A data engineer has to be an expert in programming languages like Python and SQL, ETL tools, data storage, and cloud environments.
TL; DR: Data engineers gather, prepare, and store data for analytics readiness——a foundational step for a career in data engineering.
Your role in the team
An often-asked question is – What is the difference between data science and data engineering? Data scientists aggregate and analyze the data captured in the pipelines created and maintained by data engineers to power decision-making across the organization. Organizations with greater maturity round out their teams with Artificial Intelligence, Machine Learning, and Deep Learning capabilities to deploy advanced predictive and cognitive techniques for business growth and leadership.
Before rapid digitization amped up data volumes, data scientists oversaw the systematic gathering of raw data for analytics initiatives. However, given its growing complexity and criticality to a company's competitiveness, data engineering has evolved into a separate function.
Another key contributor to the data and analytics function is the data architect, who designs the data architecture blueprint for the organization, drawing upon a close understanding of business needs. As you can see, there is a degree of overlap in the roles of data engineers, data architects, and data scientists, and they work together to provide data-driven fuel for business profitability and strategic direction.
TL; DR: Data architects envision the data infrastructure for a business. Data scientists analyze the data to help drive the business. Data engineers, data architects, and data scientists are conversant with one another's roles and cooperate closely.
Is data engineering a good career?
Despite global economic volatility and its impact on IT hiring, data engineering roles are seeing an uptick. In India, for instance, one of the world's hottest markets currently, employers are looking to hire for data engineer jobs while they may have filled the ranks for other IT jobs. Another survey of U.S. and Indian industry stakeholders listed data engineering roles as one of the top three in-demand roles.
Given the centrality of data for analytics, AI, and many more emerging technologies, the demand for data engineers is not a passing trend. By honing your skills in data manipulation, database management, and programming languages like Python and SQL, you can position yourself for a successful and sustainable career in this field. A data engineering career can serve as a gateway to continuous professional growth. But as in any career, it's not only about the skills. So here is a timeline of goals to guide your journey.
TL; DR: The demand for data engineer roles is growing and will continue to grow due to the increasing importance of data. Data engineering skills count, but so does a solid plan.
7 Goals to Cement Your Success as a Data Engineer
Mastering Data Engineering Fundamentals: This is your first short-term goal when you start to build a data engineering career. You should acquire a solid foundation by understanding data architecture, data storage technologies, data pipelines, data quality, and data governance. If your current background is in software engineering or a quantitative area such as engineering, physics, mathematics, or statistics, it will help you get off the starting block a bit faster.
How can you do this?
- Enroll in data engineering courses from reputed providers such as Udemy.
While choosing a course, keep in mind:
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- Durationand schedule – For instance, a longer course may be easier if you work or study full-time. Consider whether your current commitments will allow you to finish projects and take tests on schedule.
- Syllabus– Does it cover the basics and ongoing developments or focus on either? Any topic the course covers should be presented in depth and clarity.
- Placements – Does the provider connect you to opportunities when you finish the course? This may be very important if you do not have existing networks.
- Modes of engagement – Is it online, in-person, or blended? Does the faculty comprise academicians, practitioners, or both?
- Credits – Some courses can boost your degree or your career track more significantly than others.
- The fee – An important consideration. If you are trying to make up your mind and do not want to invest large sums yet, there are good freedata engineering courses available.
- Research – Visit communities on forums like Reddit and Github to see what the industry values today. Ask your professors, managers, or co-workers. An establishment may offer an additional course or upskilling program if enough people request it.
TL; DR: Rather than worrying about the best data engineering course, choose one that fits your current situation, career goals, and funds. Basic courses may also be needed if you lack an IT/quantitative background.
Developing Deeper Expertise
Once your fundamentals are sound, you should build upon them by sharpening your proficiency in the various tools and techniques and garnering know-how about the environments in which data engineering operates. Data engineers often choose the path of becoming generalists or focus on certain dimensions like data storage or pipelines. You can go ahead with one or two additional deep dives in the areas that interest you to further your career in data engineering.
How can you do this?
- Build your muscle with daunting tasks in programming languages like Python and SQL.
- Find real-life case studies and implementations.
- Connect one-to-one with the experts who teach you in your company or on the Internet.
- Deliberate if related certifications in areas like Six Sigma or data science will give you an edge.
- Become extremely familiar with the nuances of cloud platforms like AWS and Azure by pursuing courses like AWS Data Engineering (Course ID: 2330150) and Azure Data Engineering (Course ID: 221070).
TL; DR: Don't settle for a single certification. See how you can add value to it.
Building a Portfolio of Projects
Like in any career, practical experience is crucial for a career in data engineering. Your course may have required you to work on live projects. However, adding a few more to your resume has many benefits and will get you noticed faster.
How can you do this?
- You can find internships through your educational institution, course provider, personal network, or with your current employer.
- Contributing to open-source projects is also a proven way to challenge your newly acquired data engineering skills in real-world situations.
- You do not have to restrict your projects and internships to specific domains, giving you a wider choice.
- Consider unpaid opportunities if it is financially feasible.
TL; DR: Projects and internships are necessary to attract employers. Get started during your course. Source them through many channels and keep going.
Specializing in a Domain
As you gain expertise, consider specializing in a particular domain, such as healthcare, BFSI, or e-commerce. You will understand the subtleties of data in that domain, and can help your future organization and colleagues know their market better and develop a competitive advantage.
How can you do this?
- Select domains that interest you, that are poised for growth, and identify a few at the intersection.
- Approach companies that lead in these domains or are the best analytics partners for companies in these domains for internships.
- Look for workshops, training programs, and talks in your chosen domain.
- If possible, you can specialize in more than one domain but too many may not be a great idea.
TL; DR: Gain domain expertise to unlock business value quickly in your new data engineering career.
Continuous Learning and Upskilling
Continuous learning is key to a successful career in data engineering. In fact, it applies to any career, especially in today's times when technology disruption is a given. It's essential to stay up-to-date with the latest technologies and trends.
How can you do this?
Attend industry conferences, webinars, and workshops.
- Choose an employer that encourages innovation and collaboration in the workplace.
- Acquire new certifications when you can.
- Learning and upskilling also include moving out of your comfort zone.
- Serve a new geography, use your skills for CSR projects, or try and move into a related function like data science or data architecture. Also, consider a stint in unrelated teams like business development or sales where your technical expertise can lead to wins. There may be financial benefits as well from such moves. Data architects, for instance, have higher salaries than data engineers.
TL; DR: Never get complacent. Find various ways to stay ahead of cutting-edge developments.
Transitioning into Management or Leadership Roles
This is a long-term goal. With experience and proven capabilities, you can aspire to advance into management or leadership positions. While many technical people aspire to leadership roles, some prefer to remain individual contributors. Even if you are of such a bent, helming teams and/or projects can give you fresh perspectives on communication and cooperation and how they can spark faster and better innovation.
How can you do this?
Ensure it is a part of your career track where you work.
- Get a post-graduate qualification in management.
- Be open to leadership roles in functions outside data engineering as you grow in seniority. For example, the top echelons in your team may not need new people, but you may be asked to own a data and analytics project for your company's HR division – something you don't know much about. Taking up the offer will teach you many new things, transforming you as a professional.
TL; DR: Seek opportunities to lead and own, as this will develop you as a well-rounded professional.
Contributing to the Data Engineering Community
Make the time to share your knowledge and expertise without expecting returns. You will probably have more to share as you grow in your career but less time to do so. But paying forward helps the profession. And it will boost your visibility and reputation. Additionally, stepping back from business-as-usual and interacting with people across the experience and business function spectrums will give you new viewpoints about the field and where it is headed. This will keep you passionate and curious, the qualities that drive growth in any career.
How can you do this?
Contribute to open-source projects.
- Step up to train and mentor people within and outside your company.
- Participate in online forums and speak at offline events.
- Look for projects at work that are research-oriented and push the boundaries.
- Publish research findings and thought leadership.
TL; DR: Give back without thought for financial returns. Boost personal branding and community growth.
Bottomline: A data engineering career is a solid, sustainable option in a world that increasingly depends on data.
This is the perfect time to kick-start your data engineering career. Choose the right course and build on it with advanced learning and internships. Recce the market carefully and reach out to employers who foster innovation and growth and do best-in-breed work at a global level. And build your reputation as a thought leader.
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