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Junior Data Engineer vs Data Analyst / Analytics Engineer — which is a better entry path for me?
Hello everyone,
I’m seeking guidance on choosing the right entry-level data career path.
I want to understand whether preparing for junior Data Engineer roles would be a better option for me compared to Data Analyst or Analytics Engineer roles, especially in terms of getting an entry-level/junior role more easily.
My background:
B.Sc. in Electronics and communications (graduated in 2020)
Volunteered full-time at a non-profit organization since 2021
Last 2 years: managing events, handling emails, and maintaining Excel spreadsheets (tracking data, coordination, reporting)
I currently have a career gap in terms of formal industry roles and want to transition into the data field. I’m open to learning required technical skills and building projects.
My questions:
Is aiming for a junior Data Engineer role more realistic than Data Analyst or Analytics Engineer roles for someone with my background?
Which role has a lower entry barrier for freshers or career switchers?
What learning path would you recommend based on my experience?
Any advice would be greatly appreciated. Thank you!
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11 answers
Updated
Laila’s Answer
With your B.Sc. in Electronics and Communications, you're well-prepared for a technical data career. It may not be about finding an "easier" path, but rather a more strategic one that fits your skills.
Here's what I suggest:
While many people can enter the Data Analyst field, it's quite competitive. Instead, consider focusing on Analytics Engineering or Junior Data Engineering. Your background in ECE gives you strong systems thinking skills, which many people from non-technical fields don't have. Look for opportunities in Telecom, IoT, or Hardware companies. These industries will appreciate your degree and see your engineering skills as a valuable asset.
In today's job market, having basic skills isn't enough. Learn to use AI tools for automating data tasks, like cleaning and building pipelines. This will show you're ready for the future. Don't view your non-profit work as a gap; it's valuable experience in Operational Reporting. Highlight your Excel and coordination work as managing data integrity and lifecycle to show your professionalism.
Aim for a role in Analytics Engineering. It's a great fit between the reporting you've done and the advanced technical work your degree has prepared you for.
Master SQL & Python
Target Telecom/Tech
Here's what I suggest:
While many people can enter the Data Analyst field, it's quite competitive. Instead, consider focusing on Analytics Engineering or Junior Data Engineering. Your background in ECE gives you strong systems thinking skills, which many people from non-technical fields don't have. Look for opportunities in Telecom, IoT, or Hardware companies. These industries will appreciate your degree and see your engineering skills as a valuable asset.
In today's job market, having basic skills isn't enough. Learn to use AI tools for automating data tasks, like cleaning and building pipelines. This will show you're ready for the future. Don't view your non-profit work as a gap; it's valuable experience in Operational Reporting. Highlight your Excel and coordination work as managing data integrity and lifecycle to show your professionalism.
Aim for a role in Analytics Engineering. It's a great fit between the reporting you've done and the advanced technical work your degree has prepared you for.
Laila recommends the following next steps:
Updated
Shilpa’s Answer
Hello – I’d suggest finding your passion area amongst these first. Entry-level hiring in data is primarily driven by skills and role accessibility. Given your background in excel-based tracking, reporting, and coordination, aiming for a Data Analyst (DA) role is more realistic than a junior Data Engineer (DE) role, which typically has a higher technical and systems engineering bar.
DA roles generally have a lower entry barrier for career switchers and accept non-traditional experience more readily. I would recommend focusing on strengthening SQL, advanced Excel, and a BI tool (Power BI or Tableau), and seeing how your existing volunteer work can be reframed into data-driven projects.
Review job postings to identify common skill requirements, prioritize hands-on projects over certifications, as employers value demonstrated capability more than formal credentials at the entry level.
Good luck with whatever you choose!!
DA roles generally have a lower entry barrier for career switchers and accept non-traditional experience more readily. I would recommend focusing on strengthening SQL, advanced Excel, and a BI tool (Power BI or Tableau), and seeing how your existing volunteer work can be reframed into data-driven projects.
Review job postings to identify common skill requirements, prioritize hands-on projects over certifications, as employers value demonstrated capability more than formal credentials at the entry level.
Good luck with whatever you choose!!
Thankyou for your time !
Anjali
Updated
Swati’s Answer
Hi Anjali -
This is a good question, and you’re thinking about it the right way.
In general, Data Analyst has the lowest entry barrier, followed by Analytics Engineer, and then Data Engineer. Junior Data Engineer roles exist, but they usually expect a stronger engineering background (CS fundamentals, pipelines, cloud tools), which can be tougher without prior industry roles.
Based on your background, Data Analyst is the most realistic entry path. Your experience with Excel, tracking data, reporting, and coordination is more relevant than it may feel, and many entry-level analyst roles focus on organizing data, answering questions, and communicating insights rather than heavy engineering. Analytics Engineer is a great next step once you’ve built solid SQL and data modeling skills. Data Engineer can still be a long-term goal, but it’s often easier to move there after gaining experience as an analyst.
If you’re open to learning, a good path would be:
- Strengthen Excel and SQL
- Learn basic Python for analysis
- Pick a BI tool (Tableau / Power BI) and build a few projects
- Clearly present your nonprofit work and projects as real data experience
Good luck!
This is a good question, and you’re thinking about it the right way.
In general, Data Analyst has the lowest entry barrier, followed by Analytics Engineer, and then Data Engineer. Junior Data Engineer roles exist, but they usually expect a stronger engineering background (CS fundamentals, pipelines, cloud tools), which can be tougher without prior industry roles.
Based on your background, Data Analyst is the most realistic entry path. Your experience with Excel, tracking data, reporting, and coordination is more relevant than it may feel, and many entry-level analyst roles focus on organizing data, answering questions, and communicating insights rather than heavy engineering. Analytics Engineer is a great next step once you’ve built solid SQL and data modeling skills. Data Engineer can still be a long-term goal, but it’s often easier to move there after gaining experience as an analyst.
If you’re open to learning, a good path would be:
- Strengthen Excel and SQL
- Learn basic Python for analysis
- Pick a BI tool (Tableau / Power BI) and build a few projects
- Clearly present your nonprofit work and projects as real data experience
Good luck!
Updated
Divyanshu’s Answer
Certifications can also help you in getting a Junior Data Engineer or Data Analyst job.
Target an Alteryx certification, it is a powerful code-free data analytics tool.
Use the following link to start learning for free:
https://www.alteryx.com/sparked/learning-programs/students
I am recommending the Alteryx certification because:
- It is a widely recognized tool
- There are free online lessons and certifications provided by Alteryx
- Knowledge gained from Alteryx certification is useful in various data jobs
- Main Alteryx benefits:
1. Faster analytics workflows (automation)
Automates repetitive data prep and reporting steps so analysts spend less time wrangling data and more time analyzing.
2. Low-code / no-code data prep
Drag-and-drop workflow building makes it accessible to non-engineers while still powerful enough for advanced users.
3. Connects to lots of data sources
Pulls data from common databases, cloud apps, files (CSV/Excel), data warehouses/lakes, and more—helpful for unifying scattered data.
4. Repeatable and auditable processes
Workflows are reusable and easier to document than ad-hoc scripts/spreadsheets, improving governance and reducing human error.
5. Advanced analytics & predictive capabilities
Supports geospatial, statistical, and predictive modeling (and can integrate with R/Python), enabling deeper insights beyond basic BI.
Target an Alteryx certification, it is a powerful code-free data analytics tool.
Use the following link to start learning for free:
https://www.alteryx.com/sparked/learning-programs/students
I am recommending the Alteryx certification because:
- It is a widely recognized tool
- There are free online lessons and certifications provided by Alteryx
- Knowledge gained from Alteryx certification is useful in various data jobs
- Main Alteryx benefits:
1. Faster analytics workflows (automation)
Automates repetitive data prep and reporting steps so analysts spend less time wrangling data and more time analyzing.
2. Low-code / no-code data prep
Drag-and-drop workflow building makes it accessible to non-engineers while still powerful enough for advanced users.
3. Connects to lots of data sources
Pulls data from common databases, cloud apps, files (CSV/Excel), data warehouses/lakes, and more—helpful for unifying scattered data.
4. Repeatable and auditable processes
Workflows are reusable and easier to document than ad-hoc scripts/spreadsheets, improving governance and reducing human error.
5. Advanced analytics & predictive capabilities
Supports geospatial, statistical, and predictive modeling (and can integrate with R/Python), enabling deeper insights beyond basic BI.
Updated
Armando de Jesús’s Answer
Hi Anjali,
Given your background, transitioning into the data field is absolutely possible, and the best entry point depends on how quickly you want to break in and how technical you want your path to be. Junior Data Engineer roles tend to have the highest barrier to entry because they require strong skills in Python, SQL, cloud platforms, and data pipeline tools, which can take time to build from scratch. For someone with a career gap and experience mainly in Excel, reporting, and coordination, Data Analyst roles are generally the most accessible starting point, since they focus on Excel, SQL, data visualization tools, and communicating insights, these are areas that already overlap with your recent responsibilities.
Analytics Engineer roles sit between Analyst and Data Engineer, requiring strong SQL and data modeling but less infrastructure work, making them a natural next step once you gain confidence as an analyst.
A practical learning path for you would be to start with Data Analytics, build a few solid projects in Excel, SQL, Power BI or Tableau, and basic Python, then move toward Analytics Engineering if you enjoy the technical side, and eventually explore Data Engineering once you’ve built a stronger foundation. This approach lets you enter the field sooner while still keeping the door open for more advanced roles later.
If you’re open to learning new technical skills, I strongly recommend starting with Python early in your journey. It’s the most widely used programming language in data roles, and learning it now will make every path (Data Analyst, Analytics Engineer, or Data Engineer) much easier to navigate later. Python will help you automate tasks, clean and analyze data, work with APIs, and eventually build more advanced projects if you decide to move toward engineering. Even a basic foundation in Python gives you a competitive advantage and opens the door to more opportunities as you grow in the field
Given your background, transitioning into the data field is absolutely possible, and the best entry point depends on how quickly you want to break in and how technical you want your path to be. Junior Data Engineer roles tend to have the highest barrier to entry because they require strong skills in Python, SQL, cloud platforms, and data pipeline tools, which can take time to build from scratch. For someone with a career gap and experience mainly in Excel, reporting, and coordination, Data Analyst roles are generally the most accessible starting point, since they focus on Excel, SQL, data visualization tools, and communicating insights, these are areas that already overlap with your recent responsibilities.
Analytics Engineer roles sit between Analyst and Data Engineer, requiring strong SQL and data modeling but less infrastructure work, making them a natural next step once you gain confidence as an analyst.
A practical learning path for you would be to start with Data Analytics, build a few solid projects in Excel, SQL, Power BI or Tableau, and basic Python, then move toward Analytics Engineering if you enjoy the technical side, and eventually explore Data Engineering once you’ve built a stronger foundation. This approach lets you enter the field sooner while still keeping the door open for more advanced roles later.
If you’re open to learning new technical skills, I strongly recommend starting with Python early in your journey. It’s the most widely used programming language in data roles, and learning it now will make every path (Data Analyst, Analytics Engineer, or Data Engineer) much easier to navigate later. Python will help you automate tasks, clean and analyze data, work with APIs, and eventually build more advanced projects if you decide to move toward engineering. Even a basic foundation in Python gives you a competitive advantage and opens the door to more opportunities as you grow in the field
Thank you for the advice, Armando de Jesús.
Anjali
Updated
Vibha’s Answer
With your background, starting as a Data Analyst is a great and achievable goal. You already have valuable experience with Excel, tracking data, and creating reports, even if it wasn't in a formal data role. These skills easily transition into entry-level data analyst jobs, which often focus on SQL, Excel, basic data cleaning, and sharing insights. On the other hand, junior Data Engineer roles usually require more technical skills, like building data pipelines, working with cloud platforms, and using Python, which can be challenging without industry experience.
To move forward, consider enhancing your SQL skills, improving your Excel and visualization abilities with tools like Power BI or Tableau, and creating small projects that highlight real-world analysis, similar to your work with non-profits. Once you secure a data analyst position and gain experience, you can gradually explore roles in analytics engineering or data engineering if that interests you. This step-by-step approach is more manageable for career changers and sets a solid foundation for long-term success in the data field.
To move forward, consider enhancing your SQL skills, improving your Excel and visualization abilities with tools like Power BI or Tableau, and creating small projects that highlight real-world analysis, similar to your work with non-profits. Once you secure a data analyst position and gain experience, you can gradually explore roles in analytics engineering or data engineering if that interests you. This step-by-step approach is more manageable for career changers and sets a solid foundation for long-term success in the data field.
Updated
Nehaba’s Answer
Hi Anjali,
Given your background, aiming for a Data Analyst role first offers the lowest entry barrier and a practical pathway to build skills. Over time, you can evolve into Analytics Engineer or Data Engineer roles as you deepen your technical expertise. This stepwise approach maximizes your chances of landing your first formal industry role and succeeding in the data field.
Given your background, aiming for a Data Analyst role first offers the lowest entry barrier and a practical pathway to build skills. Over time, you can evolve into Analytics Engineer or Data Engineer roles as you deepen your technical expertise. This stepwise approach maximizes your chances of landing your first formal industry role and succeeding in the data field.
Thank you, Nehaba!
Anjali
Chinyere Okafor
Educationist and Counseling Psychologist
1205
Answers
Updated
Chinyere’s Answer
Hi Anjali,
I want to start by saying that you are not "behind." Even if they weren't in a formal tech capacity, you've been working, contributing, and developing practical skills. That counts and matters. Let's now look at this strategically and cut through the distractions.
To put it simply, compared to Junior Data Engineer, Data Analyst (or Analytics Engineer-lite) is now the most practical and easy entry path for you. This just shows a more intelligent sequencing strategy is being used, but it does not mean that data engineering is no longer an option.
This is the reason. Strong programming skills in Python, Java, and Scala, advanced SQL, databases, data pipelines, cloud platforms, and occasionally distributed systems are usually required for junior data engineer positions. Companies often look for applicants who already think like engineers and have some production-style experience, even at the "junior" level. This makes Data Engineering a more expensive starting point and a more difficult transition for those looking to change careers.
However, positions as a data analyst are far more similar to what you presently do. You may immediately apply your knowledge of Excel, data tracking, reporting, coordination, and sharing ideas. Many entry-level analyst positions emphasize SQL, Excel, basic Python, data cleansing, and visualization and are designed for new hires and career switchers. At this level, the hiring market is just more flexible.
An analytics engineer falls in the middle. It combines data modeling, analytics, and SQL-intensive transformation operations (typically with tools like dbt). It's a fantastic position, but it often requires that you have prior experience as a data analyst. Therefore, consider it step two rather than step one.
So, the ranking usually looks like this in terms of entry barrier:
- Lowest barrier: Data Analyst
- Medium: Analytics Engineer
- Highest barrier: Data Engineer
Let's now explore the learning path, which is where your transition can be far less risky. Start by strengthening your foundational knowledge of data analysis, including advanced Excel, SQL (which cannot be compromised), a single visualization tool such as Power BI or Tableau, and basic Python. Create simple yet understandable projects that demonstrate insights rather than just code, tidy up complex data, and provide genuine answers to problems. This is an amazing collection of your nonprofit experience; create case studies from your previous spreadsheets and reporting work.
You can add analytics engineering abilities like data modeling, dbt-style transformations, and more structured SQL after you're comfortable and have a portfolio. Pipelines, cloud tools, and backend concepts can then be added if you're still interested in data engineering. You'll be moving from within the data ecosystem at that point, which is far simpler than breaking in from the outside.
One final, important change in mindset is to go for the fastest, most credible entry point rather than the "best" position. Your initial function serves as a launch rather than your final objective. Entering the field of data analysis, establishing your credibility, and then advancing laterally or upward is a common and highly effective plan.
You have a solid foundation since you're asking the right questions, you're willing to learn, and you already have relevant experience. You'll be shocked at how quickly things start to click if you focus on momentum more than perfection.
Best wishes!
I want to start by saying that you are not "behind." Even if they weren't in a formal tech capacity, you've been working, contributing, and developing practical skills. That counts and matters. Let's now look at this strategically and cut through the distractions.
To put it simply, compared to Junior Data Engineer, Data Analyst (or Analytics Engineer-lite) is now the most practical and easy entry path for you. This just shows a more intelligent sequencing strategy is being used, but it does not mean that data engineering is no longer an option.
This is the reason. Strong programming skills in Python, Java, and Scala, advanced SQL, databases, data pipelines, cloud platforms, and occasionally distributed systems are usually required for junior data engineer positions. Companies often look for applicants who already think like engineers and have some production-style experience, even at the "junior" level. This makes Data Engineering a more expensive starting point and a more difficult transition for those looking to change careers.
However, positions as a data analyst are far more similar to what you presently do. You may immediately apply your knowledge of Excel, data tracking, reporting, coordination, and sharing ideas. Many entry-level analyst positions emphasize SQL, Excel, basic Python, data cleansing, and visualization and are designed for new hires and career switchers. At this level, the hiring market is just more flexible.
An analytics engineer falls in the middle. It combines data modeling, analytics, and SQL-intensive transformation operations (typically with tools like dbt). It's a fantastic position, but it often requires that you have prior experience as a data analyst. Therefore, consider it step two rather than step one.
So, the ranking usually looks like this in terms of entry barrier:
- Lowest barrier: Data Analyst
- Medium: Analytics Engineer
- Highest barrier: Data Engineer
Let's now explore the learning path, which is where your transition can be far less risky. Start by strengthening your foundational knowledge of data analysis, including advanced Excel, SQL (which cannot be compromised), a single visualization tool such as Power BI or Tableau, and basic Python. Create simple yet understandable projects that demonstrate insights rather than just code, tidy up complex data, and provide genuine answers to problems. This is an amazing collection of your nonprofit experience; create case studies from your previous spreadsheets and reporting work.
You can add analytics engineering abilities like data modeling, dbt-style transformations, and more structured SQL after you're comfortable and have a portfolio. Pipelines, cloud tools, and backend concepts can then be added if you're still interested in data engineering. You'll be moving from within the data ecosystem at that point, which is far simpler than breaking in from the outside.
One final, important change in mindset is to go for the fastest, most credible entry point rather than the "best" position. Your initial function serves as a launch rather than your final objective. Entering the field of data analysis, establishing your credibility, and then advancing laterally or upward is a common and highly effective plan.
You have a solid foundation since you're asking the right questions, you're willing to learn, and you already have relevant experience. You'll be shocked at how quickly things start to click if you focus on momentum more than perfection.
Best wishes!
Updated
Sandeep’s Answer
If you enjoy coding, consider becoming a Data Engineer. If coding isn't your thing, a Data Analyst role might be better. Focus on learning Excel too, as it will be very helpful. It's also a great idea to get a Master's Degree if you can.
Thanks for your comment. Can you suggest me universities to get a master's degree?
Anjali
Nothing specific. Most of the colleges have this. I have seen Jain college, Christ, St. Joseph, BITS Pilani all.
Sandeep Chenthamara
Updated
Carrie’s Answer
Follow your interests. You mentioned three choices: Data Analyst, Data Engineer, and Analytics Engineer. It sounds like an Analytics Engineer role combines the work of both a Data Analyst and a Data Engineer, which could be efficient. Experiment with a database to see if building pipelines and storage appeals to you. If not, get a dataset from Kaggle to clean, transform, and visualize. If you enjoy that, consider becoming a Data Analyst.
Also, if you have a particular industry you are interested in, it would be best to start gaining some useful background knowledge that can provide context to the datasets you may work with. For example, if you want to do stock forecasting, understand the mechanics and key terms for investment and economics. I found that even if you have the skills, many job descriptions would still like for you to have some understanding of industry terms and knowledge.
Also, if you have a particular industry you are interested in, it would be best to start gaining some useful background knowledge that can provide context to the datasets you may work with. For example, if you want to do stock forecasting, understand the mechanics and key terms for investment and economics. I found that even if you have the skills, many job descriptions would still like for you to have some understanding of industry terms and knowledge.
I appreciate this, thank you for the advice.
Anjali
Updated
Arpit’s Answer
I was in a very similar spot—BTech ECE, then pivoted into data after my master’s, and it’s been a fun (and very learn-by-doing) ride.
For career switchers, Data Analyst is usually the lowest entry barrier, Analytics Engineer next, and Data Engineer is typically harder at junior level due to infra + backend expectations.
Given your Excel + ops + coordination background, I’d start with Data Analyst → Analytics Engineer, build projects, volunteer for data ownership, communicate your impact well, and transition to DE later if you enjoy pipelines and systems.
For career switchers, Data Analyst is usually the lowest entry barrier, Analytics Engineer next, and Data Engineer is typically harder at junior level due to infra + backend expectations.
Given your Excel + ops + coordination background, I’d start with Data Analyst → Analytics Engineer, build projects, volunteer for data ownership, communicate your impact well, and transition to DE later if you enjoy pipelines and systems.