Ultimate Data Analytics Career Roadmap

From Data Analyst to Data Engineer as Individual Contributed (IC)

I understand that getting your first job can be challenging. You have to manage many things, and it feels like a chicken-and-egg problem.

To land a job, you need experience; to get experience, you need a job. This is the most common dilemma. I will address this issue in another post. This is something we constantly do at Surfalytics, helping people align with their goals and actually land the job.

I've identified three categories of individuals, and depending on their needs, the action plan varies.

Let's identify the groups:

1. Those with no actual data experience who want to land a job. For these folks, I am using a Data Analyst (BI developer) roadmap.

2. Those with experience as an analyst or who believe they possess key analyst skills and are ready to move towards a more engineering-focused role or transition from a non-technical to a technical (engineering) job.

3. Those who may already have some experience in the data space but want to maximize their total compensation.

In this newsletter, I will share six roadmaps for a data career. Despite the rise of Generative AI, we shouldn't worry about being replaced any time soon. However, the risk is real. If you doubt it, you can read two books that aren't new:

• Angel: How to Invest in Technology Startups--Timeless Advice from an Angel Investor Who Turned $100,000 into $100,000,000

• Zero to One: Notes on Startups, or How to Build the Future

These books are excellent. They will bring you back down to earth and help you adopt a more realistic perspective.

Road map 1: Data Analyst or BI developer

If you don't have any real experience in data and you want to start your career in data the simplest way is doing so to target role of Data/Product/Marketing/Finance/etc Analyst or BI Developer.

If you would need more context on this topic, I’ve recorded several lessons, that will bring clarity for you.

Overall course information:

What is Analytics:

Goal of Analytics:

Analytics Framework:

Roles in Analytics:

✅ What to do?

You don't need any special education or special Analytics university program. All what you need is a common sense and a bit of knowledge of spreadsheets (Excel, Google Sheets, etc).

✅ Why spreadsheets are important?

It is simple. They have Columns, Rows and basically looks similar like tables.

✅ Why common sense is important?

It would help you make judgment on data you and help stakeholders to make a right decisions.

✅ The road map has a order of progression:

1. You will start learn SQL. When to stop learn it? Simple test - you take a SQL task and solve it with pan and paper. This is the most important technical skill.

2. Business Domain. You need to understand what kind of metrics you want to calculate or how existing metrics are calculated. The easiest domain is Marketing and Product because almost every company measure their digital marketing efforts and usage of the product. Not sure where to start, start from "Lean Analytics" book.

3. Think about ways of communication. The best is writing and visualization. The goal is to communicate your findings with stakeholders and help them make a right decision.

4. As soon as you comfortable with SQL, you need to have hands on with databases. You don't need to deploy or maintain database, but you should be comfortable of using it and exploring data. In 95% cases it would be relational (SQL) database. In rare cases could be NoSQL.

5. Last crucial skill is proficient knowledge of at least single BI tool from developer perspective. You can progress towards BI Administrator and learn how to manage BI Server, how to manage users and security and optionally do performance optimization.

6. Modern analytics solution are hosted in the cloud. Be ready to know at least one public cloud of your choice: AWS, Azure, GCP.

7. I've highlighted Statistics. It is optional and often important for marketing and product analytics use cases especially for A/B testing use cases.

✅ How long it takes to acquire the skills?

In my experience, it takes 3-4 months. It takes time to sink in your head and be comfortable.

✅ How to land first job?

Practice SQL and BI. Do little projects. But most important apply the jobs. You have to fail at least 10 interviews to get a sense expectations and identify the gaps. Celebrate every interview failure! 🍾

Hint: never use "junior" 🤐

✅ What is salary expectations?

Depends on Region. For example, Canada, US, Australia, New Zealand the range per year is 90k-140k in local currencies. More you practice with interviews, higher compensation 🤗

PS You may curious why I can talk about other countries outside of Canada where I am based. There are 3 reasons:

1. My favourite question for anyone - How much you makes in you role/company/city? I collect this stats for last 15 years. Just a useful habit.

2. At Surfalytics, we are community with transparent approach about compensation and cost of living. The goal is simple know you worth.

3. I am running Cider Testing and Job Market overview, you can checkout last 2 episodes:

New Zeland Job Market:

Australia Job Market:

✅ Where to progress after Data Analyst?

Moving towards Analytics and Data Engineering or Sr/Staff Analyst. I highlighted with "yellow".

Road Map 2: Analytics Engineer

As a data analyst or BI developer with good SQL skills and expertise in at least one BI tool, you might wonder what comes next after mastering dashboard creation and stakeholder communication.

You could aim for a senior analyst position or transition into a management role. However, it's important to consider your value to the organization, your unique skills, and job security. Many people in the industry see moving into a technical (engineering) role as a great next step after a year in analytics and BI roles.

Let's talk about advancing to an Analytics Engineer role. I view Analytics Engineer as a bridge between Data Engineer and BI Developer roles, although the exact responsibilities can vary by organization. This role often focuses on "Analytics as Code" and using the "Modern Data Stack."

Let's use our analyst background to our advantage and transition into analytics engineering, building on the data analyst skills we already have.

✅ Modern Data Stack Flavour

I hope it is clear, the often buzz words can help. In case of Analytics Engineer fancy tools like dbt, mode, sigma, hex, fivetran and others defiantly ruling the market.

✅ Cloud Data Warehouse

You should know cloud already and your next move is to dive deep into at least one popular cloud DW such as Snowflake, BigQuery, Redshift. Can you build a table? Can you load a data? Can you do a deep dive? Any performance improvements?

✅ Data Modelling

You shouldn't be an expert. But you should know theory of Dimensional. Modelling, understand Slow Change Dimensions, Snapshots and so on. As Analytics Engineer you have to understand business requirements and convert them into DW model using SQL and available modelling techniques.

✅ dbt

dbt is a hype king in the modern data stack fest. Apart from hype this tool is great way of organizing your SQL transformations into "models", i.e. model your data and derive actual metrics that you will use in reporting. Learn it with cloud data warehouse of your choice.

✅ Engineering Excellence

Analytics Engineering role is technical and it requires you to work with the code, even just SQL and YAML files. That's why it is important to learn Git version control systems and understand well key DevOps techniques. This is your competitive advantage. You think about code quality, data quality, deployments to production and etc.

✅ Low Code/No Code ETL tools

By default this role assume, that data is in cloud data warehouse. But again it depends on the organization and their perspective of data roles. Better know how to extract data for source applications and Low Code/No Code tools. There are two primary topics Orchestration and Ingestion.

✅ Code

Python is a best choice and it will open path towards open source tools for BI and Data Engineering and this is next level.

✅ Where to grow?

Obviously in data engineering (green).

✅ What is salary expectations?

Depends on Region. For example, Canada, US, Australia, New Zealand the range per year is 120k-180k in local currencies.

Road Map 3: Data Engineer

You know how to turn business requirements into data pipelines, conduct code reviews, and grasp the fundamental concepts of Data Warehousing. In essence, you can deliver value to business stakeholders and have a wide range of data tools at your disposal.

So, what's next?

Let's explore the roadmap for a Data Engineer.

Assuming you're proficient with Data Analyst Skills and
Analytics Engineer Skills.

And you're ready to learn new skills.

✅ DevOps and Infrastructure

DevOps knowledge is crucial for creating secure and scalable data infrastructure. This includes working with containers, CI/CD, Infrastructure as Code, and understanding networking to protect key data assets and deploy solutions in production.

✅ Cost of Solution

Public clouds help track solution costs. It's important to create analytics solutions that are both high-performing and cost-effective.

✅ Architecture

Understand the origins of data (i.e., data sources) and business needs. Your goal is to devise a solution architecture that securely and efficiently meets these requirements.

✅ Cloud Data Warehouse

Become an expert in distributed data warehouses, optimization, and performance tuning. You should be skilled in designing solutions and data modeling.

✅ Quality and Monitoring

Learn to build quality pipelines and utilize testing frameworks and monitoring solutions.

✅ Apache Spark

While not the only tool, Spark is essential for data engineering and enjoys widespread use.

✅ Lakehouse and Data Lake

Besides data warehouses, you should be familiar with Data Lakes and Lakehouses, understanding their advantages and knowing when to use each.

✅ Streaming

Beyond batch processing, it's sometimes necessary to develop streaming solutions using Kafka, Flink, Debezium, or public cloud services.

✅ Salary Expectations

Salaries vary by region. For example, in Canada, the US, Australia, and New Zealand, the annual range is $140k-$250k in local currencies, with FAANG companies potentially offering higher compensation and stock options.

✅ Career Progression

Beyond engineering, you can choose to become a people manager, focus on a Staff level role within your organization, or enhance your skills in machine learning to collaborate effectively with ML teams.

Bonus Roadmaps

If you believe the only roles in classic analytics are Data Analyst, BI Developer, Analytics Engineer, and Data Engineer, think again.

I've already shared roadmaps for these common roles above.

But there are more specialized roles with a different focus on data skills.

Let's dive in.

✅ Support Engineer
When data tools break, who fixes them? The Support Engineer. This role is often undervalued but offers unique benefits:
-> No projects or deadlines, just ticket-based support with Service Level
-> Agreements (SLAs) to assist customers.
-> Influence on the product roadmap and feature releases.
-> Significant time dedicated to learning new features, including beta and previews.
-> Exposure to a variety of industries and architectural frameworks.
-> High impact on customers, with the potential for rapid career progression.
-> Independence, measured by ticket resolution time.
-> Development of soft skills and conflict resolution abilities.

The main challenge? Adhering to SLAs, especially for initial responses. You should stay available during your shift, no excuses.

✅ Sales Engineer
Beyond the cold emails and calls of sales teams lies the Sales Engineer, a tech-savvy partner who assists with product demos and answers deep technical queries.

Past benefits have included:
->Continuous learning in both sales and technical areas.
->Opportunities to travel and meet new people.
->Presenting at conferences and business events.
->Focus on demos and proofs of concept, avoiding long-term implementation projects.

The downside? The travel requirement, which often involves trips outside your city.

✅ Evangelist and Product Advocate
If you're passionate about building data communities, storytelling about products, presenting at conferences, and writing blog posts, this role could be a perfect fit.

Evangelists are highly visible and can sometimes feel like industry rock stars. It's a role suited for those truly obsessed with their work and the products they represent.

Getting into this role typically requires active community involvement and strong vendor relationships.

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Wow, you scroll to the bottom? I have actually the video with explanation for roadmaps: