So you’re an aspiring data analyst, browsing the web for job opportunities. Or perhaps you’re looking to hire a data analyst and need to know what to include in your job ad. Either way, navigating the data analytics job market can be tricky—especially when you throw data science into the mix, and realize just how often the two are confused.
In this guide, we walk you through some of the commonalities of data analytics job descriptions. We’ll highlight the best places to find data analytics jobs, and teach you how to spot relevant roles. We’ll also identify the differences between entry-level, mid-level, and senior-level roles, which can be perplexing for anybody, but especially if you’re unfamiliar with the industry terminology.
- What’s the difference between data analyst and data scientist job descriptions?
- Where’s the best place to look for data analytics jobs?
- What skills and requirements will I see in data analyst job ads?
- Junior data analyst job descriptions
- Mid-level data analyst job descriptions
- Senior data analyst job descriptions
- What other jobs can you get once you’re a qualified data analyst?
- Key takeaways
Now, let’s dive deeper.
1. What’s the difference between data analyst and data scientist job descriptions?
If you’ve run a quick search for data analyst jobs, you’ll have already noticed that lots of data scientist roles come up. Data analyst and data scientist are distinct roles, but the terms are sometimes used synonymously. We’ll get to more on that later, but below we highlight the difference between the two.
What is a data scientist?
Data science is a broad, complex, multidisciplinary field. While it has many guises, it mostly focuses on modeling data to formulate critical business questions. Data scientists tend to be experts in a particular area of study, and will usually have a Ph.D. in a subject like computer science, physics, math, artificial intelligence, or robotics. A data scientist’s role incorporates many practical and technical skills, from project management and team leadership to machine learning, data analytics, and software engineering.
What is a data analyst?
While data analytics fits neatly under the broader umbrella of data science, it is definitely a distinct discipline of its own. A data analyst carries out a defined process. This involves collecting, tidying, analyzing, and visualizing data, and sharing actionable insights with decision-makers. A data analyst’s skills can be used in any number of roles, from product analyst to marketing specialist. This is part of the reason why you’ll often see data scientists and data analysts described in the same way on job boards.
In the context of the job market, the distinction between data analysis and data science is not always clear-cut. However, we’ll explore this in more detail in section three.
2. Where is the best place to look for data analytics jobs?
Nobody enjoys job hunting. The mere fact of deciding which site to use can be enough to break a cold sweat! Rest reassured, though—it’s not as intimidating as it seems. You just need to know how to narrow down your options.
While job hunting, the first thing to consider is which site to use. Data analysts work in any number of industries, from finance to automotive and retail. They also work in several key business areas, known as ‘domains’ (a word to keep an eye out for, as you’ll see it a lot). These areas, or ‘domains’, are broad. They include anything from sales and marketing to product design and cybersecurity. So, the type of site you choose will depend on your skill level and your domain of interest.
For entry-level positions, we recommend the usual suspects—Glassdoor,Monster, Indeed, or LinkedIn. These popular sites advertise tonnes of data analyst jobs. Be aware though, because they generally focus on entry-level positions, the competition is tough. For this reason, it’s important to have a good data analytics portfolio.
Meanwhile, more specialist roles are usually available on more specialist job sites. For instance, if you’re seeking a mid- or senior-level data analytics position, you can try one of these niche job boards:
- Kaggle jobs — ideal for machine learning jobs.
- GitHub jobs — offering general tech jobs, including some data analytics roles.
- Amazon Jobs — at any given time, Amazon has hundreds of data analyst roles.
- Analyticsjobs.co.uk — a site that is UK-based, but with global job listings.
- Big Data Jobs — if you want a job related to big data, you’ll find it here.
- Jobs for R-users — a great place for R-related jobs in many fields, including data analytics.
This is just a small handful of the many options available. As you progress, you’ll learn which sites cater best to your needs. It’s also worth keeping an eye out for the different job titles that crop up, as not all data analyst jobs have ‘data analyst’ in the title. Common alternatives include data scientist (as discussed above), data engineer, product analyst, machine learning engineer, business intelligence analyst, and performance marketer. More on this in section eight.
3. What skills and requirements will I see in data analyst job ads?
As we’ve discussed, data analytics and data science are two different disciplines, but in the job market, this distinction is often blurred. The messy truth is that the terms are still evolving and that the roles share many skills, which is confusing. Firms and recruitment professionals don’t always have an in-depth understanding of what the role requires, or they get the terms mixed up, which only confuses matters further.
With that said, there are several key skills and requirements common to every data analyst. We’ll get into the detail of what’s required for junior, mid-level, and senior data analysts in the following sections. For now, though, this is what you need to know.
The first is a practical matter. As a highly technical role, even at entry-level, you’ll need some sort of qualification or training as evidence that you have the necessary skills to be a data analyst. This includes proficiency in tools and languages such as Tableau, SQL, Excel, and Python, as well as data visualization. You might acquire these skills through a master’s or a Ph.D., or through a data analytics certification.
Beyond technical expertise, good analysts have many other key traits. These include a strong head for math and statistics, a deep interest in problem-solving, and exceptional attention to detail. You’ll also be methodical, accurate in your work, detailed in your documentation, and an excellent researcher. Add to this a flair for critical thinking and a desire to collaborate with others, and you’ve got all the ingredients to be a successful data analyst.
Next up, we’ll look at some common data analyst job descriptions for junior, mid-level, and senior roles.
4. Junior data analyst job descriptions
Below, we offer a glimpse of the skills and experience you can expect to see in a typical entry-level data analyst job description. This will usually be aimed at someone with 0-2 years’ experience in the field.
Junior data analyst job descriptions: Tasks and responsibilities
- Collecting and managing data, including exploratory data analysis (EDA).
- Identifying trends and patterns in complex datasets.
- Quality assurance and data cleansing, using MS Excel.
- The ability to write basic scripts using Python.
- Reviewing and refactoring code in Python.
- Learning your domain (e.g. product design or finance) to better understand the business and to help make recommendations.
- Developing automated processes for data scraping.
- Producing dashboards, including graphs, tables, and other visualizations.
- Creating presentation decks using PowerPoint (or similar).
- Providing written reports of your findings.
Junior data analyst job descriptions: Skills
- Fluent in Python and SQL.
- Excellent database management skills, especially using MS Excel.
- Knowledge of data collection/cleaning tools, e.g. pandas (Python library).
- Experience using Jupyter Notebook to present processes and findings.
- Familiarity with online support resources, e.g. Stack Exchange or GitHub.
- Visualization expertise, including Python libraries (such as Matplotlib).
- Strong communication skills, for liaising with project leaders and teams.
- Working well as part of a team and taking direction well.
Junior data analyst job descriptions: Nice-to-haves
- Familiarity with additional languages, e.g. R.
- Some software engineering and system design knowledge.
- Some understanding of big data tools/platforms, e.g. Apache Spark.
Entry-level data analyst job descriptions often focus mostly on soft skills, i.e. how you communicate, how you work with people, and so on. If you don’t tick all the technical boxes, it’s not necessarily a deal-breaker. At entry-level, good employers are usually far more interested in hiring someone with the right mindset and an enthusiasm to learn. Skills can be learned. A can-do attitude is much harder to find.
5. Mid-level data analyst job descriptions
Next up, we’ll look at the skills and experience you should expect to find in your average mid-level data analyst job description. This is in addition to many of the responsibilities outlined for a junior analyst. These roles are typically aimed at someone with at least two years’ experience in the field.
Mid-level analyst job descriptions: Tasks and responsibilities
- Managing analytics projects from start to finish (data integration, analysis, reporting).
- Working closely with the team leads to solve statistical business problems.
- Developing and coding new algorithms to meet specific business needs.
- Carrying out statistical research, prototyping new systems, and finding new ways of gathering, cleaning, and analyzing data.
- Managing, maintaining, and developing the organization’s data platform.
- Consulting with internal teams and external clients to determine their ongoing business needs, and to find solutions to them.
- Actively working to identify improvements to internal processes, to improve conversions, revenue, ROI (or other relevant metrics).
- Ability to lead teams in researching or solving business-critical problems.
Mid-level data analyst job descriptions: Skills
- BA or masters in computer science, information systems, mathematics, machine learning, or similar (or a data analytics certification acquired through a specific program).
- 2-5 years of experience in database and project management, including programming, data mining, analysis, and reporting.
- Strong technical skills, including Python, SQL, R, Tableau.
- Ability to create databases from scratch and to develop existing frameworks.
- Experience working with large structured and unstructured datasets.
- Exposure to platforms such as Hadoop ecosystem, e.g. Spark, Pig and Hive.
- Relevant domain expertise, i.e. in-depth knowledge of your business area.
- Familiarity with a wide variety of Python libraries.
Mid-level data analyst job descriptions: Nice-to-haves
- Some knowledge of machine learning/deep learning, including Python libraries like Tensorflow and PyTorch.
- Understanding of voice and video data.
- Knowledge of speech to text and conversational analysis tools.
As you can see, a mid-level data analyst’s job description isn’t so different from an entry-level one. The main disparity lies in the level of responsibility that falls on a mid-level analyst’s shoulders. They also need a greater level of technical expertise. Whereas a junior analyst usually reports back to their project lead, a mid-level data analyst will often be responsible for the entire data analytics process. They’ll also be expected to use their initiative to solve novel problems and on occasion may even manage a team.
It’s worth noting the nice-to-haves, too: most of the ‘desirable’ skills for a junior role become more or less essential for a mid-level data analyst. This includes software engineering skills, familiarity with at least two programming languages, and knowledge of big data tools. Meanwhile, the nice-to-haves for mid-level analysts focus on understanding more complex data types, deep learning, and machine learning (skills that are generally required more for senior roles, as we’ll see below).
6. Senior data analyst job descriptions
Finally, let’s explore the expertise you can expect to find in a senior data analyst job description. Job ads like these are typically aimed at someone with 5+ years’ experience in the field, as well as those with a postgraduate qualification.
Senior data analyst job descriptions: Tasks and responsibilities
- Directing, organizing, and leading all data analytics projects.
- Establishing data analytics processes, roles, quality criteria, and performance metrics.
- Managing the technical design, development, and delivery of new data analytics tools.
- Recruiting, hiring, and training new team members from junior level up.
- Reviewing and approving project plans, timelines, deliverables, and managing resources.
- Reviewing and approving data models, frameworks, and architecture.
- Advocating data analytics practices and technology to senior stakeholders.
- Ensuring that the organization’s data analytics strategy aligns with its wider objectives.
- Selecting and implementing (or overseeing implementation) of all data analytics tools and frameworks.
- Responsibility for overall data governance, preparing and warehousing, as well as reporting and advanced delivery, e.g. via portals and mobile.
Senior data analyst job descriptions: Skills
- MA or Ph.D. in computer science, machine learning, business intelligence, or similar.
- At least five years’ experience in a mid-level data analytics role (including clear professional development).
- Broad domain skills, i.e. knowledge of finance, HR, marketing, sales, etc.
- Knowledge of a wide range of data models, algorithms and statistical analysis techniques.
- Experienced in Agile software development.
- Experienced in building and deploying machine learning models and working with big data.
- Familiar with a wide range of business intelligence and analytics tools.
As this sample job description highlights, a senior data analyst moves away from the granular detail of data analytics and takes a broader view. The role is more of a management position, and takes responsibility for an organization’s overall data governance and strategy. It also requires strong people management skills—both for junior teams and also senior staff, such as board members or executive personnel.
While skills such as machine learning and artificial intelligence are usually relevant to a senior data analyst’s role, they are often considered jobs in their own right. That’s why we’ve taken a more cautious approach here—data science, machine learning, and artificial intelligence are distinctive fields and are often the next step for a senior data analyst who wants to take their career to the next level. But it’s good to know that the sky’s the limit!
7. What other jobs can you get once you are a qualified data analyst?
In section 2, we mentioned that not all jobs will have ‘data analyst’ in the title. This is because the role varies depending on the organization and the domain (or business area) you work in. For most positions, data analysis is one of several key skills you’ll need. Below, we highlight a few more job titles to keep an eye out for once you’ve qualified as a data analyst.
A healthcare analyst works with important health and medical data. Beyond this, the role can be surprisingly varied. Healthcare analysts may work in hospitals, evaluating data in order to improve the way patient services are delivered. They also work in related industries, such as health insurance, big pharma, or medical device production. Depending on the exact role, a healthcare analyst might create systems to collect novel data, suggest new record-keeping practices, or analyze vast amounts of data to inform the design and creation of new medical products. Big data is booming in the fast-changing healthcare industry, and the number of healthcare analyst roles is subsequently exploding.
Operations analysts work as part of the broader operations team within a business. Put simply, this means they’re responsible for helping the business run effectively on a day-to-day basis. The operations analyst role involves identifying practical problems relating to the way a business operates, and solving them. This can involve collecting and analyzing vast amounts of data from across sales, manufacturing, distribution, or engineering. Obviously, the exact area will vary depending on the type of business. Ultimately though, operations analysts have to cooperate with a variety of different teams and disciplines in order to do their job effectively.
If you love getting into the detail and creating improved systems and processes, then the role of business analyst might be for you. While there’s quite a lot of crossover with operations analysis, a business analyst’s main focus is on improving an organization’s internal workings. For instance, this might involve using data to optimize the organization’s structure, reporting, project methodologies, or hiring processes (to list just a few examples). A key part of the business analyst’s role is not just to make recommendations but to implement them, too. As such, the role is highly strategic and involves working at a high-level, either within your own business or with third-party clients. You can learn about the differences between a data analyst and a business analyst here.
Customer data analyst
Another role that’s increasingly sought after is the customer data analyst. Customer analysis involves understanding customer demographics, needs, and satisfaction. It often involves creating customer segmentations in order to determine trends, create new sales strategies, make customer recommendations, tailor marketing campaigns, or inform the creation of new products. While the role often tends to support sales and marketing, it is actually extremely varied and involves working with many different aspects of a business. For this reason, customer data analysts are well-placed to launch their career in a variety of different directions. It’s also a highly sought after role in big tech, with companies like Amazon and Facebook always seeking to hire new people for this role.
Sports data analyst
In a field where performance is everything, data is revolutionizing the sports landscape. In professional sport especially, data is now informing everything from individual athlete performance to team performance, sports medicine, and even marketing. A hugely varied role, sports analysts gather, collect, and analyze data from a wide variety of sources. This can include viewer numbers for television or online broadcasts, weather data, and wearable devices (worn by athletes). The role can be applied to almost any sport you can imagine from baseball to football, swimming, cycling, and more. Your expert insight could be used to help create new training schedules, support commentators during a live broadcast, or simply find new trends in sport.
Above are just some of the routes you could pursue once you qualify as a data analyst. As you can see, it’s an extremely varied and fascinating field; once you’ve mastered the fundamental skills, you have the opportunity to enter into a range of different industries and roles—it all depends on where your interests lie.
8. Key takeaways
In this post, we’ve offered a broad taster of the data analytics jobs available out there. We’ve looked at how they differ, what their similarities are, and how best to interpret tricky job descriptions. Whether you’re applying for a junior, mid- or senior-level data analyst role, any good data analyst needs to have:
- Knowledge of math and statistics
- Problem-solving skills
- Superb attention to detail
- A methodical approach to work
- Good research abilities
- Critical thinking skills
- Collaboration skills
- The relevant qualification
Remember, if you don’t meet every technical requirement in a job description, don’t fret. As long as you’re proficient in the core skills, tools, and languages, possess the right soft skills, and can demonstrate a willingness to learn, you’re well-equipped to apply for a data analyst position.
To learn more about a career in data analytics, try our free, five-day data analytics short course, or read more: