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How can I prepare and strategize to land an internship in Analytics Engineering?
I’m trying to break into Analytics Engineering and would appreciate guidance. I have a 5-year educational gap, but during that time I worked in a non-profit organization. Now I want to transition into this field and I’m aiming for at least an internship to start.
What should I focus on learning , and what strategies/projects would help me stand out despite my gap? Any tips on building a portfolio or presenting myself better to recruiters would mean a lot.
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10 answers
Updated
Wong’s Answer
Hi Anjali. Breaking into the Analytics Engineering field needs a smart plan. The best way to get a job, even with a gap in your education, is to use your past non-profit work to your advantage.
First, you need to focus on learning the most important skills. You must get really good at SQL, it's the language of data. After that, learn how to organize data and use a tool called dbt, which helps clean up data for others to use.
To stand out, build a portfolio that shows your skills. You can:
- Work on projects where you clean, analyze, and visualize data.
- Build simple data pipelines (where data moves from one place to another).
- Use cloud platforms to work on real-world problems.
Even though you have a gap, your experience in a non-profit is valuable. Skills like teamwork, problem-solving, and project management are important in any field. Talk about how your non-profit work involved data analysis or helped improve processes, as these skills are useful for Analytics Engineering.
If you get an interview, practice coding problems and be ready to talk about your projects. Employers want to know how you think and solve problems, so explain your projects clearly and show how you learned from them. Hope this helps.
First, you need to focus on learning the most important skills. You must get really good at SQL, it's the language of data. After that, learn how to organize data and use a tool called dbt, which helps clean up data for others to use.
To stand out, build a portfolio that shows your skills. You can:
- Work on projects where you clean, analyze, and visualize data.
- Build simple data pipelines (where data moves from one place to another).
- Use cloud platforms to work on real-world problems.
Even though you have a gap, your experience in a non-profit is valuable. Skills like teamwork, problem-solving, and project management are important in any field. Talk about how your non-profit work involved data analysis or helped improve processes, as these skills are useful for Analytics Engineering.
If you get an interview, practice coding problems and be ready to talk about your projects. Employers want to know how you think and solve problems, so explain your projects clearly and show how you learned from them. Hope this helps.
This was super helpful, thank you!
Anjali
You are welcome!
Wong Loke Yuen
Can you suggest me any useful online course or bootcamps and internship suggestions?
Anjali
Coursera offers professional certificates from top companies like Google and IBM. The Google Advanced Data Analytics Professional Certificate and the IBM Data Analyst Professional Certificate are excellent options.
Wong Loke Yuen
Updated
Prajwal’s Answer
Learn Core Skills: SQL, Python, dbt, Airflow, data modeling, and cloud warehouses (Snowflake/BigQuery).
Build Projects: Create end-to-end pipelines with open datasets, showcase on GitHub/portfolio.
Understand Business: Learn key metrics (revenue, churn, conversion) and practice explaining data clearly.
Network Smartly: Use LinkedIn, referrals, and career fairs to connect with Analytics Engineers.
Prep for Interviews: Expect SQL challenges, data modeling exercises, and case-style questions.
Build Projects: Create end-to-end pipelines with open datasets, showcase on GitHub/portfolio.
Understand Business: Learn key metrics (revenue, churn, conversion) and practice explaining data clearly.
Network Smartly: Use LinkedIn, referrals, and career fairs to connect with Analytics Engineers.
Prep for Interviews: Expect SQL challenges, data modeling exercises, and case-style questions.
Thankyou for sharing your perspective Prajwal. Can you explain me more about the interview prep part please, I really need help in this?
Anjali
Updated
Lin’s Answer
Transitioning careers is challenging, but your background in a non-profit gives you a unique understanding of real-world problems—a major asset in the data world. Here's a direct plan to help you land an internship.
At its heart, an Analytics Engineer's job is to turn messy, raw data into a clean, trustworthy foundation for analysis. You are the architect who brings order and reliability to data.
1. Core Skills & Required Depth
Focus on grasping these concepts and tools. The goal for an internship is a strong foundation, not encyclopedic knowledge.
1. Schema Design: This is the blueprint for organizing data. You need to understand the core concept, the star schema, and be able to design logical models. The goal is thoughtful design, not deep architectural theory.
2. SQL: This is the language for shaping data. You need to move beyond basics to confidently handle complex queries like joins, CTEs, and window functions. The goal is proficiency, not perfection.
3. dbt & A Cloud Warehouse (e.g., BigQuery): This is the modern toolkit for building your data models. You should be able to build a complete project from start to finish. The goal is to show you can manage the core workflow.
4. Python & Git: These are supporting skills. You need basic Python to fetch data and Git to manage your code. The goal is functional ability, not expert-level programming.
2. Your Strategy: The Portfolio Project
This is how you bridge your past experience with your future career.
The Concept: Don't pick a random topic. Build a project that solves a problem you recognize from your non-profit work, for example, analyzing donor behavior or campaign effectiveness.
The Action: Create one complete, end-to-end project. Show how you took a real question, designed a data model to answer it, built it with dbt, and created a simple dashboard.
The Focus: This project is about showcasing your problem-solving process. A thoughtful, well-documented project is more impressive than a technically complex but generic one.
3. Your Pitch: Connect Your Past to Your Future
When you talk to recruiters, tell a compelling story.
Frame Your Journey: You aren't starting from scratch. You're a problem-solver who is now learning the technical skills to make a bigger impact.
Lead with Your Project: Your resume and interviews should highlight your project, as it's tangible proof that you can do the work.
Your unique path is a strength. Companies need people who can connect data to the real world.
At its heart, an Analytics Engineer's job is to turn messy, raw data into a clean, trustworthy foundation for analysis. You are the architect who brings order and reliability to data.
1. Core Skills & Required Depth
Focus on grasping these concepts and tools. The goal for an internship is a strong foundation, not encyclopedic knowledge.
1. Schema Design: This is the blueprint for organizing data. You need to understand the core concept, the star schema, and be able to design logical models. The goal is thoughtful design, not deep architectural theory.
2. SQL: This is the language for shaping data. You need to move beyond basics to confidently handle complex queries like joins, CTEs, and window functions. The goal is proficiency, not perfection.
3. dbt & A Cloud Warehouse (e.g., BigQuery): This is the modern toolkit for building your data models. You should be able to build a complete project from start to finish. The goal is to show you can manage the core workflow.
4. Python & Git: These are supporting skills. You need basic Python to fetch data and Git to manage your code. The goal is functional ability, not expert-level programming.
2. Your Strategy: The Portfolio Project
This is how you bridge your past experience with your future career.
The Concept: Don't pick a random topic. Build a project that solves a problem you recognize from your non-profit work, for example, analyzing donor behavior or campaign effectiveness.
The Action: Create one complete, end-to-end project. Show how you took a real question, designed a data model to answer it, built it with dbt, and created a simple dashboard.
The Focus: This project is about showcasing your problem-solving process. A thoughtful, well-documented project is more impressive than a technically complex but generic one.
3. Your Pitch: Connect Your Past to Your Future
When you talk to recruiters, tell a compelling story.
Frame Your Journey: You aren't starting from scratch. You're a problem-solver who is now learning the technical skills to make a bigger impact.
Lead with Your Project: Your resume and interviews should highlight your project, as it's tangible proof that you can do the work.
Your unique path is a strength. Companies need people who can connect data to the real world.
This is great! I really needed this. Thankyou for answering my question Lin.
Can I ask for suggestions from you if I get stuck or need guidance in future?
Anjali
Updated
Raghu’s Answer
Hello Anjali,
Following are the steps that I would go about to land an internship in Analytics and Data Engineering:
Technical skills to develop
1) Mastery in SQL - This is a foundational skill. Practice complex queries, window functions, CTEs, and performance optimization. Work with real databases like BigQuery Sandbox, PostgresSQL, Databricks, Snowflake, etc...
2) Python/R for Data - Learn pandas, numpy, and visualization libraries like matplotlib, or plotify. Focus on data cleaning, transformation, and basic statistical analysis.
3) dbt (data build tool) - Many companies use dbt for analytics engineering. Learn the basics of modeling, testing, and documentation. The courses (freely available) are excellent.
4) Version control - get proficient with Git, GitHub. Most engineers work collaboratively on code that is tracked and deployed.
Portfolio
Create 2-3 projects that demonstrate end-to-end workflows. Good project ideas include analyzing public datasets (sports, finance, or government data), building dashboards or creating data pipelines.
Document your work clearly with README files, explaining your process and findings
Gain relevant experience
If you are working, look for opportunities to work with data in your current role. If not, look for coursework and/or volunteer opportunities.
Application strategy
Target companies in different stages - startups often provide broad exposure to the full analytics stack, while larger companies offer mode structured environments conducive to mentorship.
Don't only apply to tech companies. In the current day, analytics is an important part of companies all over the industrial landscape.
Interview prep
Practice SQL problems on platforms like HackerRank, LeetCode, or StrataScratch. Be ready to walk through your code in detail, and explain technical decisions.
Prepare for behavioral questions about problem solving, and working with stakeholders . Analytics engineers typically are an important bridge between technical and business teams.
This is a rapidly evolving, and growing field. So, curiosity about this field, where it is going, and the tools will set you apart. Focus on building practical skills rather than theoretical knowledge.
Following are the steps that I would go about to land an internship in Analytics and Data Engineering:
Technical skills to develop
1) Mastery in SQL - This is a foundational skill. Practice complex queries, window functions, CTEs, and performance optimization. Work with real databases like BigQuery Sandbox, PostgresSQL, Databricks, Snowflake, etc...
2) Python/R for Data - Learn pandas, numpy, and visualization libraries like matplotlib, or plotify. Focus on data cleaning, transformation, and basic statistical analysis.
3) dbt (data build tool) - Many companies use dbt for analytics engineering. Learn the basics of modeling, testing, and documentation. The courses (freely available) are excellent.
4) Version control - get proficient with Git, GitHub. Most engineers work collaboratively on code that is tracked and deployed.
Portfolio
Create 2-3 projects that demonstrate end-to-end workflows. Good project ideas include analyzing public datasets (sports, finance, or government data), building dashboards or creating data pipelines.
Document your work clearly with README files, explaining your process and findings
Gain relevant experience
If you are working, look for opportunities to work with data in your current role. If not, look for coursework and/or volunteer opportunities.
Application strategy
Target companies in different stages - startups often provide broad exposure to the full analytics stack, while larger companies offer mode structured environments conducive to mentorship.
Don't only apply to tech companies. In the current day, analytics is an important part of companies all over the industrial landscape.
Interview prep
Practice SQL problems on platforms like HackerRank, LeetCode, or StrataScratch. Be ready to walk through your code in detail, and explain technical decisions.
Prepare for behavioral questions about problem solving, and working with stakeholders . Analytics engineers typically are an important bridge between technical and business teams.
This is a rapidly evolving, and growing field. So, curiosity about this field, where it is going, and the tools will set you apart. Focus on building practical skills rather than theoretical knowledge.
Thank you, this is really helpful.
Anjali
Updated
Amy’s Answer
Hi Anjali, analytics engineering is a promising career field, you've made a great choice! I recommend considering entry level roles such as "data analyst" or "business intelligence engineer" to gain relevant experience and then applying to data engineering roles. The reason I recommend this approach is because many data engineering roles look for prior experience in data-adjacent roles. Alternatively, you can raise your hand to volunteer for data projects in your current job the nonprofit. This might give you content to discuss in interviews for your desired role. Best wishes with your career pivot, you got this!
Updated
Ronny’s Answer
On the technical side, learning SQL is a must. This helps you get introduce on the world of data structures and gives you the basis on how languages are used to process data.
Along with learning and understanding SQL you'll need to invest some time in understanding data structures and how they can be implemented for different use cases.
After that you can start reading about ETL and ELT concepts. Then it will be good to get familiar with some ETL tool, maybe some cloud solution and experiment on creating processes for loading some csv files into a schema you built. I recently used snowflake on cloud and I think it's a friendly tool for learning.
Along with learning and understanding SQL you'll need to invest some time in understanding data structures and how they can be implemented for different use cases.
After that you can start reading about ETL and ELT concepts. Then it will be good to get familiar with some ETL tool, maybe some cloud solution and experiment on creating processes for loading some csv files into a schema you built. I recently used snowflake on cloud and I think it's a friendly tool for learning.
Updated
Ingrid’s Answer
Hi Anjali,
I see a lot of great tips on the technical aspects of starting to get into the field. I will chime in on other aspects to think about.
Take a step back and think about dialing into what type of job(s) you're interested in and the industry(s). One thing that has helped me tremendously in my own career is to approach the job search backwards. Meaning that first I tried to understand my own interests and what types of roles I was potentially interested in. From there I started to talk with folks in those roles / industries to see if it aligned with what I was thinking. I would reach out to folks on LinkedIn or in-person through professional networking groups or meetups in that space. The personal coffee chats were so helpful because I asked people about their careers, how they landed into their roles, what experience / skills were most helpful, and if they knew of any open roles either at their own company or other companies. In this way I began to increase my network. At the end of each coffee chat I would end each conversation with one question "Who do you know that I should be speaking with?" After our conversation they knew what I was looking for, the role or the industry, and I was able to expand that search. You can imagine that this took quite a bit of time and effort. It did. I ended up doing probably over 60 coffee chats but through that I learned more and more about the careers I was interested in pursuing. I got tips on how to improve my resume, on what skills to improve, and increased my network. The most helpful thing is that I would get introductions to teams, to companies, that were personalized because I had gotten to know the folks. When you're applying for jobs it can be demoralizing because you're one person among many many. This approach takes a while, it takes effort, but without a doubt I have found it to be much more helpful. Believe me that as you start to have these conversations you'll get more insight into the practical things that you can do to make yourself a stronger candidate.
Let me know if I can clarify pieces about this and good luck!
I see a lot of great tips on the technical aspects of starting to get into the field. I will chime in on other aspects to think about.
Take a step back and think about dialing into what type of job(s) you're interested in and the industry(s). One thing that has helped me tremendously in my own career is to approach the job search backwards. Meaning that first I tried to understand my own interests and what types of roles I was potentially interested in. From there I started to talk with folks in those roles / industries to see if it aligned with what I was thinking. I would reach out to folks on LinkedIn or in-person through professional networking groups or meetups in that space. The personal coffee chats were so helpful because I asked people about their careers, how they landed into their roles, what experience / skills were most helpful, and if they knew of any open roles either at their own company or other companies. In this way I began to increase my network. At the end of each coffee chat I would end each conversation with one question "Who do you know that I should be speaking with?" After our conversation they knew what I was looking for, the role or the industry, and I was able to expand that search. You can imagine that this took quite a bit of time and effort. It did. I ended up doing probably over 60 coffee chats but through that I learned more and more about the careers I was interested in pursuing. I got tips on how to improve my resume, on what skills to improve, and increased my network. The most helpful thing is that I would get introductions to teams, to companies, that were personalized because I had gotten to know the folks. When you're applying for jobs it can be demoralizing because you're one person among many many. This approach takes a while, it takes effort, but without a doubt I have found it to be much more helpful. Believe me that as you start to have these conversations you'll get more insight into the practical things that you can do to make yourself a stronger candidate.
Let me know if I can clarify pieces about this and good luck!
Updated
Liqi’s Answer
Hi Anjali,
It’s awesome that you’re moving toward Analytics Engineering — your non-profit experience already shows impact and problem-solving!
Here’s where to focus:
1. Core Skills: Keep practicing SQL (joins, window functions, CTEs, etc.) on LeetCode or DataLemur. Build a Tableau Public or Power BI portfolio — Makeover Monday (https://makeovermonday.co.uk/) is a great weekly challenge.
2. Engineering Basics: Learn dbt (free at dbt website) and data-modeling concepts like star schema, fact/dimension tables.
3. Projects: Create an end-to-end pipeline — use SQL + dbt to clean data and visualize it in Tableau. Document your work on GitHub.
4. Showcase: Post dashboards, share your learning on LinkedIn, and join communities.
5. Apply smart: Target internships or data fellowships at mission-driven orgs that value your background.
Your gap doesn’t define you — your projects and curiosity do. Keep building and sharing, and you’ll stand out quickly!
It’s awesome that you’re moving toward Analytics Engineering — your non-profit experience already shows impact and problem-solving!
Here’s where to focus:
1. Core Skills: Keep practicing SQL (joins, window functions, CTEs, etc.) on LeetCode or DataLemur. Build a Tableau Public or Power BI portfolio — Makeover Monday (https://makeovermonday.co.uk/) is a great weekly challenge.
2. Engineering Basics: Learn dbt (free at dbt website) and data-modeling concepts like star schema, fact/dimension tables.
3. Projects: Create an end-to-end pipeline — use SQL + dbt to clean data and visualize it in Tableau. Document your work on GitHub.
4. Showcase: Post dashboards, share your learning on LinkedIn, and join communities.
5. Apply smart: Target internships or data fellowships at mission-driven orgs that value your background.
Your gap doesn’t define you — your projects and curiosity do. Keep building and sharing, and you’ll stand out quickly!
Updated
Ramyata’s Answer
That's such an awesome question Anjali. If I were in your shoes, here's exactly how I'd approach it:
1. Pick the tools you'll actually use: Analytics engineering is mostly about SQL, data modeling (you need to research the latest tools, dbt is hot), and a cloud warehouse (Bigquery, Snowflake, Redshift). Spend a month getting genuinely comfortable with those, not just reading tutorials but actually solving problems and building things. I personally found DataCamp super helpful when I learnt Python.
2. Turn your nonprofit work into a data story: You probably already touched data in that role (donor lists, program outcomes, budgets). Clean it, model it and present it as if you were an analytics engineer hired to make sense of this data and share your findings. That instantly becomes portfolio material.
3. Ship tiny but real projects: Instead of generic "portfolio", post two or three well documented mini projects on Github or a simple website. Show your SQL , your data model diagrams, and a quick write up of the insights. Recruiters love tangible proof of your work.
4. Network to feed your curiosity - Join dbt slack, linked groups for analytics engineers, reach out to people to get their opinion on topics or to understand their career paths if that appeals to you, people love to share their experience. Comment on posts where you can meaningfully add a perspective, ask questions that will help you learn more about that topics and share your mini projects. These all will have a compounding effect.
1. Pick the tools you'll actually use: Analytics engineering is mostly about SQL, data modeling (you need to research the latest tools, dbt is hot), and a cloud warehouse (Bigquery, Snowflake, Redshift). Spend a month getting genuinely comfortable with those, not just reading tutorials but actually solving problems and building things. I personally found DataCamp super helpful when I learnt Python.
2. Turn your nonprofit work into a data story: You probably already touched data in that role (donor lists, program outcomes, budgets). Clean it, model it and present it as if you were an analytics engineer hired to make sense of this data and share your findings. That instantly becomes portfolio material.
3. Ship tiny but real projects: Instead of generic "portfolio", post two or three well documented mini projects on Github or a simple website. Show your SQL , your data model diagrams, and a quick write up of the insights. Recruiters love tangible proof of your work.
4. Network to feed your curiosity - Join dbt slack, linked groups for analytics engineers, reach out to people to get their opinion on topics or to understand their career paths if that appeals to you, people love to share their experience. Comment on posts where you can meaningfully add a perspective, ask questions that will help you learn more about that topics and share your mini projects. These all will have a compounding effect.
Updated
Sandeep’s Answer
Best way to get into track on to profession is:
Go for a Course from reputed place with good campus assistance.
Try for internships - www.internshala.com
Try to approach small startups without salary and get initial experience. - Need to research on small companies, connect concerned people through LinkedIn. Mention your needs. Lot of small Non-funded companies wants resources but they don't have budget.
Go for a Course from reputed place with good campus assistance.
Try for internships - www.internshala.com
Try to approach small startups without salary and get initial experience. - Need to research on small companies, connect concerned people through LinkedIn. Mention your needs. Lot of small Non-funded companies wants resources but they don't have budget.
Thankyou for the advice. Can you suggest me such offline courses or colleges?
Anjali