Check list for fixing your Data Resume

Expert Tips to Tailor Your Resume for Initial Screening Success

At the Surfalytics community, we have defined a clear roadmap for your data career. One of the first steps is to choose the target role and prepare the resume to look nice.

This is an example of resume feedback from our community experts. The example below is almost the final product after several iterations. The initial version of it was useless. As a result, this particular member of Surfalytics landed the job in NY with no prior experience working in states. It took months but hard work paid off.

And this is the review of this member:

We have a dedicated Discord channel where we practise improving our Resume and avoiding any 🟥 flags.

In this post, I want to highlight low-hanging fruits that might help you save time and find a job.

Match Job Titles

Every time I review someone's resume, I can instantly spot the biggest issue - the title and experience don’t match the job posting.

Assume, you want to apply for the Data Engineer role. You take your favourite resume with the title Data Analyst and apply for Data Engineer.

Obviously, Data Analyst <> Data Engineer. This is 99% rejection. Data analyst and data engineer examples are not the worst use cases.

Some people might have different job titles on their resume

• IT helpdesk

• Front end developer

• Barista

• Couriers

• and etc

It is great that you have a non-relevant experience that might give you valuable skills BUT there is 0 relevance to the job you are applying.

Let’s assume you have this kind of Resume. In high-level summary:

Target title: Data Specialist

Location: Different Country for Job Application

Summary:
Super puper result oriented professional with 1 year of exerience and bla bla

Work Experience:
Data Specialist at Z 01.2023-05.2024
- your duties

Call Center operator at Y 06.2021 - 12.2023
- your duties

Line cook at X
- your duties

You decided to apply for Data Engineer. In my opinion, it is a waste of your time and time of HR to review your application. There are some interactions with word data, but probably that’s it.

If you are applying for a job with title X, but in your resume online title A, B, C. You have had a very low chance of getting even a call from HR.

Are you curious why? The answer is simple: The DATA profession is now a popular topic. There are hundreds of trainings, workshops, MBA and Master's degrees, and thousands of data influencers.

Everyone wants to work from home for high $$$ on their MacBook with Oat Milk latte. Reality is different.

Based on my experience people with MBAs and Masters in Analytics, or even folks with non-relevant experience are suffering in the same way. They all are struggling to find a job. Because they don’t have real skills that matter.

Instead of wasting time and money, you need to try to narrow your focus and define your specialization. Pick 1-2 job roles and make the resume 100% match.

At least, you will get a shot for a screening call with HR.

Let’s tweak a bit our resume:

Target title: Data Engineer

Location: Same country as job application (assume you have work authorisation)

Summary:
- 4 years of Data Engineering
- ....
- ...

Work Experience:
Data Engineer at Z 01.2023-05.2024
- your DATA Engineering duties

Data Engineer at Y 06.2021 - 12.2023
- your DATA Engineering duties

Data Analyst/Engineer at X 01.2020 - 06.2021
- your DATA Engineering duties

We tweaked it to look like a good Data Engineer with 4 years of experience.

Before going crazy and apply all jobs let’s make sure:

• You actually have Data Analytics or Data Engineering experience. If not, you can gain it in 4-6 weeks to prepare for job applications. That’s why at Surfalytics we run almost weekly data engineering and data analytics projects from real-world cases and practice interviews.

• Be ready to fail 5-10 real interviews before you get a job. Every failure is a big success, celebrate it. It is real feedback on your effort.

Summary of your profile

Let’s review the Summary of your resume. Almost every resume I saw has a useless summary. What a waste of time. According to research most recruiters look at the top of your resume:

They also spend ~ 6 seconds per resume. That’s why it is in hour interest to make the Summary insightful and to the point. Remove all useless verbs like result-oriented, experienced and so on.

Example of bad one:

As a Canada-based Data Engineering contractor, Dmitry holds a Master's in Engineering and has 15+ years of international experience in Business Intelligence, Data Warehousing, Big Data, Cloud, and ML. He has built data platforms with cloud and big data technologies and supported machine learning experiments, data science models, and business intelligence reporting. He adeptly manages privacy compliance and data security. He operates via Incorporation and possesses a valid Secret with the Canadian Government.

Improved one:

• 15+ years of building and leading modern Data Engineering Solutions.

• Engineered data pipelines using Amazon Redshift, AWS Glue, EMR, Databricks for processing over 100 TB of data.

• Migrated legacy analytics systems to Snowflake, dbt, Tableau, Looker.

• Managed and created data infrastructure using modern DevOps practices, Infrastructure as a Code, CI/CD.

• Mentored and hired Analytics and Data Engineering teams.

• Built Iceberg Lake House using Amazon Athena, Airbyte for FinTech company.

It is not perfect but I hope you got an idea. It is a place to make a point about your experience.

Geographic market specific

It is an important and obvious rule. You need to have the Work Authorisation in the country to which you are applying. Don’t be naive to apply to Canada and the US from India or Kazakhstan. It won’t work. There are thousands of eligible people in the country itself.

Better find a way to get a work visa officially.

Sometimes, you can try to make a trick like I did in the past. I was in Russia and was searching job in Winnipeg, Canada. Obviously, HR will turn down all my applications with non-Canadian addresses. I made a trick. I’ve added the Winnipeg Address of my friend and used IP telephony to forward it to my Russian number. My goal was to reach the hiring manager.

It worked out, I got an interview with the hiring manager and explain that I was waiting Permanent Residence card. They made an offer and waited for 6 months while my documents were processed.

The gap in your experience

Often you might be in a situation with a gap in the experience:

Target title: Data Engineer

Location: Same country as job application (assume you have work authorisation)

Summary:
- 4 years of Data Engineering
- ....
- ...

Work Experience:
Data Engineer at Z 01.2023-12.2023
- your DATA Engineering duties


Data Analyst/Engineer at X 01.2020 - 06.2021
- your DATA Engineering duties

This gap looks terrible, at least for me. Do you want to risk your lifetime opportunity to miss an interview due to this gap? At Surfalytics we worked towards closing the gap and looking into the opportunity to close the gap. But the best way is to close the gap is: to extend the date to close the gap. This looks solid:

Target title: Data Engineer

Location: Same country as job application (assume you have work authorisation)

Summary:
- 4 years of Data Engineering
- ....
- ...

Work Experience:
Data Engineer at Z 01.2023-12.2023
- your DATA Engineering duties


Data Analyst/Engineer at X 01.2020 - 01.2023 (extend the date
- your DATA Engineering duties

Alternatively, you can extend your last job till the current time.

No one really cares about your past. The most important is your skills. Are you able to perform well? Are you ready to compensate lack of skills with a 60-80-hour working week? Moreover, there are different “Privacy laws” that will protect your personal life and your past;)

There is no easy road to success and hard work will be paid off!

Tools and use cases

Another aspect of your resume - your job duties and tools. For every role, we can define 20% tools and skills that matter the most. In another article, I’ve covered this topic:

For example, you probably want to share your actual experience and duties and best thing you can do something like:

- Excel
- Reports and Data Analysis in excel
- Pivot and cross tables in excel
- etc.

The point is that you will put something that might look like a 100% Data Analytics tool but in real life, it is not true. Even if you are working as an analyst, you probably work with product managers, and data engineers at the organization and you should be aware of their work and tools:

- Building Product usage dashboards in Tableau for Product department to track key product metrics like DAU, cohort analysis
- Developed Self-Service report for Marketing manager to track Ad spent
- Work close with Data Engineering team
- Ensuring quality of busienss metrics in data engineering pipeline using dbt tests and Great Expectations
- Maintain documentation in Data Catalog
- etc

In my example, I’ve added more specific examples. My examples aren’t boring, they are actually cool and I wish to work with this person vs just “cross table pivot in Excel”.

Again, no this kind of experience? Let’s get it. By the way best book to gain knowledge for business metrics is Lean Analytics.

Every Surfalytics member starts their journey from this book 🚀.

Your Portfolio

Another great asset for you as a data professional might be thinking about the portfolio.

There are a couple of cool things you might try:

• Active GitHub account

  

• Personal Blog. But you should choose the dedicated theme. You can have a blog about “all things data”. Specialization is key in blogs, resumes and so on.

• Contribute to open-source products. Even with limited code knowledge you can read and improve Markdown files and documentation.

• Participation in data community or user groups like Surfalytics where you can run the projects and share the knowledge

• Presenting local user groups for top data vendors: Databricks, Snowflake, Microsoft, Tableau etc.

• Presenting at conferences.

Basically, anything data-related that you are passionate about and you make.

Certification and Education

Usually in the resume, we have a dedicated section for Education and Certifications. In most cases for data jobs, skills and experience matter the most. If you have high education, it is great. In case it is not relevant, just hide specialization. It is nice to have something.

If you don’t have anything, just go to Coursera and finish something relevant and call it as your education;)

The certifications are even more fun. There are tons of certs. They have zero value for anyone. Usually, the training for certification preparation like dbt, Tableau, Snowflake, Databricks, Azure, AWS and etc are great. But the passing exam is a different story. By the end of the day if you feel more comfortable with certification, just add this into your resume without passing. But make sure you have the desired skills and knowledge and at least finish the training.

How many copies of resumes do you need?

We are coming to an interesting question. I’ve heard that people are tired of tailoring their resume for each position they apply.

Fortunately, it is useless. Instead, you should prepare several resumes with dedicated specialization:

• Data Analyst Resume

  • Product Analyst Resume

  • Marketing Analyst Resume

  • Finance Analyst

  • DevOps Analyst

• Analytics Engineer

• Data ENgineer

  • Azure Data Engineer

  • GCP Data Engineer

  • AWS Data Engineer

I hope it makes sense for you that you will slightly change the tooling and use cases but the core will email the same.

My example:

PDF vs Doc

Sometimes, I can see a very nice resume built-in PDF. Unfortunately, it might be hard to read and consume by HR or ATS Systems.

Forget about fancy PDF files with bar charts and other nonsense. Let’s use simple Google Docs and simple text. My example:

My example isn’t perfect. But still a great point to start. I would also add more details about BUSINESS VALUE and actual IMPACT on business and data team.

Resume vs Linkedin

You might now wonder what to do if you have several resumes ranging from Data Analyst to Data Engineer.

Sometimes, HR or even hiring managers might quickly check your LinkedIn profile and their expectations that your LinkedIn should match your Resume. The key here is to match:

• Date ranges

• make sure the keyword “data” appears in your title.

For example, instead of Data Analytics, you can write Data at XYZ company. This simple trick will replace a family of data jobs.

Again, the best possible strategy you can get is SPECIALIZATION.

If you are curious about the difference between roles, I recommend you to check the simple data roadmap from 0 to hero data engineer:

Eh, cover letter?

I doubt anyone will check your cover letter. Don’t waste time.

Competitive Advantage

This is what we actually do at Surfalytics with our pet projects, mock interviews and overall support - we are looking for ways to gain a natural competitive advantage that will help you stand out among other candidates, let me share ideas:

• Understanding the Value of Analytics

• Knowing the pain points of Hiring Managers and proactively offering solutions

• Using the keywords specific to the role

• Understanding end-to-end architecture of analytics solutions

• Domain knowledge and key analytics use cases for the industry

• Never apply on LinkedIn. It is useless in 95%, go directly to the company website.

• Networking is a great way of learning about openings in your area. Check all user groups for Snowflake, Power BI, Tableau, Databricis, DBT and other data-related meetups and conferences!

• Try to grow your LinkedIn presence, write 2-3 posts per week. Again, think about a dedicated topic or tool.

Your primary goal is to be one of the first candidates to apply. The earlier you are in the pipeline of candidates the higher chances you have. Obviously, if you are 500+ candidates your chances are low.

Another good advice could be to leverage friends for a direct reference to the company. However, the current market is difficult and there are so many candidates.

The best thing you can do is focus on things you can control and make them look perfect. This will increase your chances of starting a conversation with the company and hiring manager.

References

• Google Doc resume template

• A practical guide to writing FAANG-ready software engineer resumes

• How to Create an Impressive GitHub Profile README

• Resume score

• Career advice for long-term career ““Essential Tips for Career Success based on real story”: Part 1, Part 2, Part 3

• Soft Skills: The software developer's life manual

  •